Apparatus for determining association variables

ABSTRACT

An apparatus, and related method, for determining one or more association variables is described. The apparatus includes at least one processor, at least one memory, and at least one program module stored in the memory configured to be executed by the processor. The program module includes instructions for selecting a subset of temporal onsets in a set of temporal onsets, instructions for determining a statistical relationship between the subset of temporal onsets and a pattern of occurrence of a variable, and instructions for identifying the variable as a migraine variable in accordance with the statistical relationship. The subset of temporal onsets includes one or more onsets corresponding to one or more migraines experienced by at least one individual, and the set of temporal onsets includes the subset of temporal onsets and one or more temporal onsets corresponding to one or more additional headaches experienced by at least the one individual.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority under 35 U.S.C. 120 as aContinuation-in-Part Patent Application of U.S. Patent application Ser.No. 11/177,063, “Apparatus for Determining Association Variables,” filedon Jul. 8, 2005 (now U.S. Pat. No. 7,223,234), and as aContinuation-in-Part Patent Application of U.S. patent application Ser.No. 11/178,044, “Apparatus for Collecting Information,” filed on Jul. 8,2005 (now U.S. Pat. No. 7,311,666), both of which claim priority under35 U.S.C. 119(e) to U.S. Provisional Application Ser. No. 60/601,480,“Medical Informatics System,” filed on Aug. 14, 2004, to U.S.Provisional Application Ser. No. 60/591,300, entitled “HealthcareProvider-Patient Interaction Management System,” filed on Jul. 27, 2004,and to U.S. Provisional Application Ser. No. 60/587,003, entitled“Medical Informatics System,” filed on Jul. 10, 2004, the contents ofeach of which are herein incorporated by reference.

This application is also related to U.S. patent application Ser. No.11/529,054, “Apparatus for Determining Association Variables,” filed onSep. 27, 2006, to U.S. patent application Ser. No. 11/604,494,“Apparatus for Determining Association Variables,” filed on Nov. 27,2006, to U.S. patent application Ser. No. 11/704,735, “Apparatus forProviding Information Based on Association Variables,” filed on Feb. 9,2007, and to U.S. patent application Ser. No. 11/809,807, “Apparatus forAggregating Individuals Based on Association Variables,” filed on May31, 2007.

FIELD OF THE INVENTION

The present invention relates generally to an apparatus, and relatedmethods, for processing data, and more specifically, for determiningstatistical relationships.

BACKGROUND OF THE INVENTION

Statistical learning problems may be categorized as supervised orunsupervised. In supervised learning, the goal is to predict an outputbased on a number of input factors or variables (henceforth, referred toas variables), where a prediction rule is learned from a set of examples(referred to as training examples) each showing the output for arespective combination of variables. In unsupervised learning, the goalis to describe associations and patterns among a set of variableswithout the guidance of a specific output. An output may be predictedafter the associations and patterns have been determined. Thesecategories are illustrated in FIGS. 1A and 1B, which show data points asa function of weight 110 and height 112. In unsupervised learning 100 inFIG. 1A, the data may be described by input variables weight 110 andheight 112 without any additional information (e.g., labels) that couldhelp to find patterns in the data. Patterns in the data may be found bylearning that there are two distinguished “clusters” of data points(represented by circles or decision boundaries 114 around them). Withineach cluster, data in group A 116 or group B 118 are highly similar(i.e., close) and between clusters data are highly dissimilar (i.e.,further away). When a new data point, i.e., combination of the inputvariables becomes available, it may be categorized as similar to andthus a potential member of one of the clusters that have beendiscovered, or as an outlier or as a member of a new cluster.

In supervised learning 130 in FIG. 1B, additional information about thedata is available. The data points are labeled as Dutch 132 (whitecircles) or American 134 (filled-black circles). This extra informationis exactly the output one wants to predict for future data. Having itavailable for the training data or examples allows predictive decisionboundary 136 to be determined. In general, statistical learning involvesfinding a statistical model that explains the observed data that may beused to analyze new data, e.g., learning a weighted combination ofnumerical variables from labeled training data to predict a class orclassification for a new combination of variables. Determining a modelto predict quantitative outputs (continuous variables) is often referredto as regression. Determining a model to predict qualitative data(discrete categories, such as ‘yes’ or ‘no’) is often referred to asclassification.

Developing models for statistical learning problems involvinglongitudinal data, in which a time series of observations are collectedover a period of time, poses several challenges, including thoseassociated with collecting the data efficiently and accurately. Analysisof the data may also be problematic, in particular, for a class ofproblems where variables associated with time-varying phenomena thathave discrete events or epochs, each epoch having a characteristic onsettime (henceforth referred to as a temporal onset), are sought. Forexample, in such problems there may be limited data and a plurality ofpotential variables to be screened. The analysis, therefore, may beunderdetermined. In addition, the potential variables may not beindependent from one another and/or samples of the potential variablesmay not have a corresponding probability distribution (for example, anormal distribution).

There is a need, therefore, for an analysis technique to address thechallenges described above and to determine variables associated withtime-varying phenomena having discrete epochs.

SUMMARY OF THE INVENTION

An apparatus, and related method, for determining one or moreassociation variables is described. The apparatus may include at leastone processor, at least one memory, and at least one program module. Theprogram module may be stored in the memory and may be configured orconfigurable to be executed by the processor. The program module mayinclude instructions for selecting a subset of temporal onsets in a setof temporal onsets; instructions for determining a statisticalrelationship between the subset of temporal onsets and a pattern ofoccurrence of a variable; and instructions for identifying the variableas a migraine variable in accordance with the statistical relationship.The subset of temporal onsets may include one or more onsetscorresponding to one or more migraines experienced by at least oneindividual, and the set of temporal onsets may include the subset oftemporal onsets and one or more temporal onsets corresponding to one ormore additional headaches experienced by at least the one individual.The determining may include contributions from presence and absenceinformation in the pattern of occurrence of the variable.

The one or more additional headaches may include one or more reboundmigraines, one or more recurrence migraines and/or one or more tensionheadaches.

The pattern of occurrence of the variable may be during a set of timeintervals. A respective time interval in the set of time intervals mayprecede a corresponding respective temporal onset in the subset oftemporal onsets. Time intervals in the set of time intervals may beoffset in time from temporal onsets in the subset of temporal onsets.

In some embodiments, the program module further includes instructionsfor excluding at least one of the temporal onsets in the set of temporalonsets from the subset of temporal onsets due to missing data in thepattern of occurrence of the variable.

In some embodiments, the pattern of occurrence of the variable includescategorical data. In some embodiments, a respective entry in a patternof occurrence of the variable is considered present if the respectiveentry approximately exceeds a threshold.

In some embodiments, the pattern of occurrence of the variable includesone or more entries corresponding to at least a time interval after atleast a respective temporal onset in the subset of temporal onsets. Arespective migraine corresponding to at least the respective temporalonset may have a duration including at least the time interval. The oneor more entries may be excluded when the statistical relationship isdetermined.

The determining may use a non-parametric statistical analysis technique,including a chi-square analysis technique, a log-likelihood ratioanalysis technique and/or a Fisher's exact probability analysistechnique. The determining may use a supervised learning techniqueincluding a support vector machines (SVM) analysis technique and aclassification and regression tree (CART) analysis technique. Thestatistical relationship may at least in part be determined using afilter, such as an analog filter and/or a digital filter.

In some embodiments, the program module further includes instructionsfor receiving information including the set of temporal onsets and thepattern of occurrence of the variable. In some embodiments, the programmodule further includes instructions for providing recommendations toone or more individuals in accordance with the migraine variable.

In some embodiments, the program module further includes instructionsfor determining statistical relationships for a plurality of variables.In some embodiments, the program module further includes instructionsfor determining a first ranking of the plurality of variables inaccordance with the statistical relationships and/or for subtracting asecond ranking from the first ranking. This second ranking maycorrespond to a background, such as noise or interference signals.

The migraine variable may be a migraine trigger that at least in partinduces a migraine in at least the one individual if at least the oneindividual is exposed to the migraine trigger. In some embodiments, theprogram module further includes instructions for associating at leastthe one individual with one or more groups of migraine triggers inaccordance with the identified migraine trigger.

In another embodiment, the method may be implemented as acomputer-program product for use in conjunction with a computer system.The computer-program product may include a computer-readable storagemedium and a computer-program mechanism embedded therein for determiningone or more migraine variables associated with migraines.

In another embodiment, a process for determining one or more migrainevariables associated with migraines is described. A first data streamincluding a set of temporal onsets corresponding to one or moremigraines and a pattern of occurrence of a variable are transmitted. Asecond data stream including information that identifies the variable asa migraine variable is received. The information may be determined inaccordance with a statistical relationship between a subset of temporalonsets in the set of temporal onsets and a pattern of occurrence of thevariable. The subset of temporal onsets may include one or more onsetscorresponding to one or more migraines experienced by at least oneindividual. The set of temporal onsets may include the subset oftemporal onsets and one or more temporal onsets corresponding to one ormore additional headaches experienced by at least the one individual.The statistical relationship may include contributions from presence andabsence information in the pattern of occurrence of the variable.

In another embodiment, a graphical user interface and related method aredescribed. The graphical user interface includes a first window toreceive and display information corresponding to a first item consumedby an individual during a first time interval, and a second window todisplay selectable second items consumed by the individual during asecond time interval. At least a portion of the second time intervalprecedes the first time interval.

In another embodiment, an apparatus, and related method, for determiningitems that include a variable is described. The apparatus may include atleast one processor, at least one memory, and at least one programmodule. The program module may be stored in the memory and may beconfigured or configurable to be executed by the processor. The programmodule may include instructions for determining first instances of thevariable in a data structure, and instructions for identifying one ormore items that include at least one of these first instances of thevariable. In some embodiments, the program module may further includeinstructions for defining the one or more identified items as subsequentversions or the variable and for repeating the operations of identifyingand determining until a number of iterations are performed, aprobability associated with items identified in a given iteration isless than a pre-determined value, or no instances of items areidentified in a given iteration.

The variable may be an ingredient and the one or more identified itemsin the operations of identifying and determining may be foods thatcontain the ingredient.

In another embodiment, a method for providing food products isdescribed. In the method, a first set of food products that contains afirst amount of at least some migraine triggers in a first set ofmigraine triggers is provided, and a second set of food products thatcontains a second amount of at least some migraine triggers in a secondset of migraine triggers is provided. The first amount is less than afirst pre-determined value and the second amount is less than a secondpre-determined value. The first set of food products may be intended forconsumption by members of a first group of individuals that respond tothe first set of migraine triggers and the second set of food productsmay be intended for consumption by members of a second group ofindividuals that respond to the second set of migraine triggers.Response to a migraine trigger in a respective set of migraine triggersmay include at least one individual having a migraine if at least theone individual is exposed to a respective amount of the migraine triggergreater than a respective pre-determined value.

The respective food product may include beverages. In some embodiments,the respective set of food products may contain a third amount of one ormore items associated with one or more of the migraine triggers in therespective set of migraine triggers. The third amount may be less than athird pre-determined value. The one or more items may include foods in afood group corresponding to one or more of the migraine triggers in therespective set of migraine triggers. In some embodiments, at least oneof the sets of food products includes a fourth amount of one or morecompounds that chemically react with one or more of the migrainetriggers in the corresponding set of migraine triggers thereby reducingan efficacy of the one or more migraine triggers to induce a migraine inat least the one individual. The fourth amount may be greater than afourth pre-determined value.

In another embodiment, a data structure includes a first variable and acorresponding first set of time intervals, and a second variable and acorresponding second set of time intervals. The first variable and thesecond variable may be associated with a medical condition in at leastthe one individual. A severity of at least one symptom associated withthe medical condition may be increased if at least the one individual isexposed to the first variable during at least one time interval in thefirst set of time intervals and the second variable during at least onetime interval in the second set of time intervals. In some embodiments,the first variable and the second variable are migraine triggers. Insome embodiments, the first variable and the second variable furtherinclude corresponding threshold quantities, in which quantities greaterthan the threshold quantities induce a migraine in at least the oneindividual.

In another embodiment, an apparatus, and related method, is described.The apparatus may include at least one processor, at least one memory,and at least one program module. The program module may be stored in thememory and may be configured or configurable to be executed by theprocessor. The program module may include instructions for associatingone or more migraine triggers, which have been determined for anindividual, with a set of pre-determined migraine triggers that areassociated with a group of individuals. The one or more migrainetriggers that have been determined for the individual may at least inpart induce a migraine in at least the individual if at least theindividual is exposed to the one or more migraine triggers. In someembodiments, the one or more migraine triggers are determined inaccordance with presence and/or absence of one or more markers in a setof markers in a biological sample from the individual.

In another embodiment, an apparatus, and related method, is described.The apparatus may include at least one processor, at least one memory,and at least one program module. The program module may be stored in thememory and may be configured or configurable to be executed by theprocessor. The program module may include instructions for recommendingone or more medicines to an individual in accordance with one or moremigraine triggers that have been determined for the individual. The oneor more migraine triggers may at least in part induce a migraine in atleast the individual if at least the individual is exposed to the one ormore determined migraine triggers. The one or more medicines may includean acute medicine that is taken during a migraine and/or a preventivemedicine that is taken during migraine attacks and between migraineattacks. In some embodiments, the one or more migraine triggers aredetermined in accordance with presence and/or absence of one or moremarkers in a set of markers in a biological sample from the individual.

The disclosed embodiments reduce or eliminate the problems describedabove and provide an analysis technique for determining associationvariables associated with time-varying phenomena having discrete epochs.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional objects and features of the invention will be more readilyapparent from the following detailed description and appended claimswhen taken in conjunction with the drawings.

FIG. 1A is a block diagram illustrating an existing unsupervisedlearning technique.

FIG. 1B is a block diagram illustrating an existing supervised learningtechnique.

FIG. 2 is a block diagram illustrating an embodiment of a system forcollecting and analyzing data, and for providing recommendations.

FIG. 3 is a block diagram illustrating an embodiment of a server orcomputer.

FIG. 4 is a block diagram illustrating an embodiment of a computer.

FIG. 5 is a block diagram illustrating an embodiment of a device.

FIG. 6A is a block diagram illustrating an embodiment of a userinterface.

FIG. 6B is a block diagram illustrating an embodiment of a userinterface.

FIG. 6C is a block diagram illustrating an embodiment of a userinterface.

FIG. 6D is a block diagram illustrating an embodiment of a userinterface.

FIG. 7 is a block diagram illustrating an embodiment of migrainetriggers and sensitivity thresholds.

FIG. 8A is a block diagram illustrating an embodiment of a questionnairedata structure.

FIG. 8B is a block diagram illustrating an embodiment of aquestionnaire.

FIG. 9A is a block diagram illustrating an embodiment of determining acompound variable associated with events having different temporalonsets.

FIG. 9B is a block diagram illustrating an embodiment of determiningcompound variables.

FIG. 10 is a block diagram illustrating an embodiment of a variableoccurring during a duration of an event.

FIG. 11 is a block diagram illustrating an embodiment of determiningmodel complexity.

FIG. 12 is a block diagram illustrating an embodiment of rankingvariables.

FIG. 13 is a block diagram illustrating an embodiment of associating oneor more variables with one or more groups of variables.

FIG. 14 is a block diagram illustrating an embodiment of a signalprocessing circuit.

FIG. 15 is a flow diagram illustrating an embodiment of a process forcollecting information.

FIG. 16 is a flow diagram illustrating an embodiment of a process fordetermining one or more association variables.

FIG. 17 is a flow diagram illustrating an embodiment of a process forproviding recommendation(s) and/or report(s).

FIG. 18 is a flow diagram illustrating an embodiment of a process forproviding one or more reports.

FIG. 19 is a flow diagram illustrating an embodiment of a process.

FIG. 20 is a flow diagram illustrating an embodiment of a process fordetermining items that include a variable.

FIG. 21 is a flow diagram illustrating an embodiment of a process forproviding food products.

FIG. 22 is a block diagram illustrating an embodiment of a questionnairedata structure.

FIG. 23 is a block diagram illustrating an embodiment of a datastructure.

FIG. 24 is a block diagram illustrating an embodiment of a datastructure.

FIG. 25 is a block diagram illustrating an embodiment of a datastructure.

Like reference numerals refer to corresponding parts throughout theseveral views of the drawings.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the present invention. However, it will beapparent to one of ordinary skill in the art that the present inventionmay be practiced without these specific details. In other instances,well-known methods, procedures, components, and circuits have not beendescribed in detail so as not to unnecessarily obscure aspects of theembodiments.

Embodiments of one or more apparatuses, and related methods, forcollecting information and determining one or more association variablesare described. The information may be collected by asking a subset ofpre-determined questions in a set of pre-determined questions one ormore times during a data-collection time interval using the one or moreapparatuses.

The subset of pre-determined questions may be varied during thedata-collection time interval in accordance with configurationinstructions received by the one or more apparatuses. In someembodiments, the configuration instructions correspond to non-executableinstructions. The configuration instructions may be transmitted to theone or more apparatuses and answers to a subset of the pre-determinedquestions, selected in accordance with the configuration instructions,may be received from the one or more apparatuses, for example, using SMSmessages and/or email messages.

The subset of pre-determined questions may include pre-selected answersthat may be displayed for each question in at least a plurality ofpre-determined questions in the subset of pre-determined questions. Insome embodiments, answering a respective pre-determined question onlyinvolves selection if the respective answer to the respectivepre-determined question is different than the respective pre-selectedanswer. The pre-selected answers may be selected in accordance with ananswer history for one or more individuals and/or one or more groups ofindividuals. In some embodiments, the pre-selected answers maycorrespond to one or more default answers.

In some embodiments, at least one apparatus or device, such as apersonal digital assistant, a tablet computer, a Blackberry, a cellulartelephone, and/or a hand-held computer, containing the set ofpre-determined questions may be provided to at least one individual. Insome embodiments, at least the one individual may be provided a memorycard (such as a smart card, a subscriber identity module or SIMS card,and/or a card having ROM, FLASH or other memory) containing the set ofpre-determined questions. In some embodiments, at least one server maytransmit instructions corresponding to the subset of pre-determinedquestions to at least one computer. A browser in at least the onecomputer may render the instructions corresponding to the subset ofpre-determined questions. Communication with apparatuses, devices,computers, and/or servers may occur via a network, such as the Internet(also known as the World Wide Web), an Intranet, a local area network(LAN), a wide area network (WAN), a metropolitan area network (MAN),and/or a wireless network.

The one or more association variables may be determined and/oridentified, using one or more apparatuses, and/or the related methods,in accordance with one or more statistical relationships between one ormore temporal onsets corresponding to one or more events and patterns ofoccurrence of one or more variables and/or one or more compoundvariables. The one or more compound variables may be determined usingone or more variables in a set of variables. The one or more temporalonsets may include one or more onset times and/or one or more onsetsduring one or more time intervals.

A respective compound variable may correspond at least to a pattern ofoccurrence of a first variable during a first time interval precedingthe one or more temporal onsets and a pattern of occurrence of a secondvariable during a second time interval preceding the one or moretemporal onsets. The first time interval and/or the second time intervalmay be offset in time from the one or more temporal onsets. The firsttime interval may be different than the second time interval. In someembodiments, the respective compound variable may correspond to patternsof occurrence of three of more variables during corresponding timeintervals preceding the one or more temporal onsets.

Contributions from presence and absence information in the pattern ofoccurrence of the one or more compound variables may be included whendetermining the one or more statistical relationships. The pattern ofoccurrence of the respective compound variable, such as the pattern ofoccurrence of the first variable and the pattern of occurrence of thesecond variable, may include presence and absence information.

The one or more statistical relationships may be determined using anon-parametric analysis technique (which makes few assumptions about anexistence of a probability distribution function, such as a normaldistribution, corresponding to a population from which samples areobtained, or regarding independence of the variables and/or the compoundvariables), an unsupervised learning analysis technique, and/or asupervised learning analysis technique. The analysis may performhypothesis testing to determine if the one or more temporal onsets andthe one or more compound variables and/or the one or more variables arestatistically independent (or dependent) in accordance with astatistical significance criterion. In the process, the analysis mayincrease an effective signal-to-noise ratio in an underdeterminedproblem (i.e., sparse sampling in a multi-dimensional variable space) byrestricting a number of local fitting neighborhoods (i.e., a number ofrelevant variables and/or compound variables).

The one or more compound variables may be ranked in accordance with theone or more statistical relationships. The variables in the set ofvariables may be ranked in accordance with a number of occurrences ofthe variables in the compound variables having respective statisticalrelationships that approximately exceed the statistical confidencethreshold corresponding to a noise floor. The statistical confidencethreshold may be selected such that at least a subset of the ranking isapproximately stable. In some embodiments, a respective ranking may becorrected using a ranking of variables and/or compound variables forwhich there is no relationship and/or for which there is an inverserelationship between the corresponding pattern of occurrence and thetemporal onsets corresponding to one or more events (which is sometimesreferred to as a background). In some embodiments, the respectiveranking may be corrected using a ranking of variables and/or compoundvariables that is determined using a random and/or a pseudorandomtemporal pattern for the temporal onsets.

One or more variables, such as the first variable and the secondvariable in the one or more compound variables, may be identified as theone or more association variables. Additional association variables maybe identified by associating the one or more association variables withone or more groups of association variables, including pre-determinedgroups of association variables. One or more recommendations may beprovided to at least the one individual in accordance with the one ormore association variables. In some embodiments, at least the oneindividual may be a healthcare provider (such as a physician, nurse,chiropractor, and/or an associated staff member), a parent, a guardian,and/or an individual that has a disease. The one or more recommendationsmay be included in one or more reports.

The one or more association variables may, at least in part, trigger,initiate, and/or precipitate the one or more events (henceforth referredto as trigger). The one or more association variables may directly orindirectly cause the one or more events. Alternatively, the one or moreassociation variables may not directly or indirectly cause the one ormore events. Instead, the one or more association variables may enablethe one or more events. To make an analogy, in some embodiments the oneor more association variables may function as keys in one or more locks(receptors), allowing a spring-loaded door (corresponding, for example,to a biochemical predisposition or reaction) to open.

In some embodiments, two or more association variables may work inconjunction with one another, i.e., at least the one individual mayexperience at least one event if at least the one individual is exposedto two or more association variables in close temporal proximity (forexample, during a time interval), in a temporal sequence and/or in anordered temporal sequence (i.e., a particular pattern of exposure to twoor more association variables). An effect of the association variablesmay be cumulative. Exposure to a sufficient quantity of the respectiveassociation variable may trigger the one or more events, or exposure toquantities of two or more association variables may trigger the one ormore events. Be it cumulative and/or cooperative, the respectiveassociation variable may correspond to 5%, 10% 25%, 50% or more of atotal trigger for the one or more events. The one or more associationvariables may be specific to an individual and/or a group of two or moreindividuals. In some embodiments, the one or more events may correspondto an episodic increase in a severity of one or more symptoms associatedwith a medical condition, a disease, such as a chronic disease, and/or adisease condition in at least the one individual.

The disease may include a form of arthritis, rheumatoid arthritis, jointdisease, an auto-immune disorder, an immune-related disorder, aninflammatory disease, lupus, thyroid disease, gout, diabetes, chronicfatigue syndrome, insomnia, depression, a psychological disease,gastrointestinal disease, colitis, ulcerative colitis, inflammatorybowel disease, Crohn's disease, candida, celiac disease, irritable bowelsyndrome, one or more food allergies, one or more food sensitivities,morning sickness, menstrual cramps, chronic pain, back pain, facialpain, fibromyalgia, asthma, migraines, abdominal migraines, cyclicvomiting syndrome, cluster headaches, chronic headaches, tensionheadaches, another type of headaches, seizures, epilepsy,neurodermatitis, acne, psoriasis, adiposity, hypertonia, heart disease,hypertension, cardiovascular disease, arteriosclerosis, a form ofcancer, and/or acquired immune deficiency syndrome. The system andmethod may also be applied to patients have multiple illnesses, such ageriatric patients.

The embodiments of the apparatuses, and related methods, may be used tocollect and analyze information associated with time-varying phenomenahaving discrete epochs. The embodiments of the apparatuses, and relatedmethods, may also be used to identify one or more association variablesfor such time-varying phenomena, thereby allowing remedial action to betaken (if appropriate). Technical effects for the embodiments of theapparatuses, and related methods, may include displaying one or morequestionnaires, including a plurality of pre-selected answers, on atleast one display, transmitting and receiving collected information(such as answers to at least a subset of the one or more questions)using a network, determining one or more statistical relationships in atleast one apparatus, identifying one or more association variables in atleast one apparatus, transmitting and receiving one or morerecommendations and/or one or more reports using the network, and/ordisplaying the one or more recommendations and/or the one or morereports on at least one display.

The embodiments of the apparatuses, and related methods, may allowcollected data or information to be converted into actionableinformation, such as one or more recommendations. In some embodiments,providing the recommendations to one or more healthcare providers and/orat least the one individual that has the disease may help convert thisinformation into knowledge. The healthcare providers, who practicemedicine, may use the knowledge to aid the one or more individuals thathave the disease, including prescribing one or more diagnostic testsand/or one or more treatment modalities (for example, a medicine). Inthe hands of at least the one individual that has the disease, theinformation may motivate behavior modification that may mitigate orreduce a severity, duration and/or frequency of one or more symptomsassociated with the disease.

Attention is now directed towards embodiments of apparatuses, devices,computers, servers, and systems that may be used to implement thecollection of information, the statistical analysis and/or the providingof recommendations. FIG. 2 is a block diagram illustrating an embodimentof a system 200 for collecting data or information, analyzing theinformation, and/or for providing recommendations (for example, in oneor more reports). A network 214 couples servers 222 and optionaldatabases 220 (located in one or more additional computers, servers,and/or network attached storage devices) to first locations 210 andsecond locations 212. The network 214 may include the Internet (alsoknown as the World Wide Web), an Intranet, a local area network (LAN), awide area network (WAN), a metropolitan area network (MAN), and/or awireless network (including one or more cellular telephone networks,Bluetooth networks, Wi-MAX networks, and/or Wi-Fi networks using IEEE802.11a, 802.11b, 802.11g and/or 802.11n).

The first locations 210 may correspond to that of one or moreindividuals or humans beings. In some embodiments, the one or moreindividuals may have been diagnosed as having the disease. At least oneof the one or more individuals (henceforth referred to as a firstindividual) may interact with at least one of computers 216 and devices218. The devices 218 may include one or more personal digitalassistants, one or more tablet computers, one or more cellulartelephones, one or more hand-held computers, and/or a combination of twoor more of these items. One or more of the servers 222 may provide thesubset of pre-determined questions one or more times during thedata-collection time interval. Pre-determined questions may includequestions that are determined prior to the beginning of thedata-collection time interval. In some embodiments, the pre-determinedquestions may be generated for at least the first individual prior tothe beginning of the data-collection time interval, for example, inaccordance with an optional initial survey and/or analysis of abiological sample taken from the first individual (as discussed furtherbelow with reference to FIG. 19). In exemplary embodiments, thedata-collection time interval may be approximately a fraction of a day(such as 1, 3, 4 or 6 hours), a day, several days, a week, a month, 2months, 3 months, 4 months, 6 months, 9 months, a year, several years,and/or a combination of one or more of these items.

In some embodiments, one or more of the servers 222 may provideinstructions for a web page corresponding to the subset ofpre-determined questions that is rendered in a browser. The instructionsfor the web page may include embedded JavaScript instructions that maybe executed by one or more of the computers 216 and/or devices 218,i.e., the one or more computers 216 and/or devices 218 may function as avirtual machine. In some embodiments, one or more of the computers 216and/or devices 218 may already contain the subset of pre-determinedquestions or the set of pre-determined questions. Configurationinstructions, which may be non-executable, from the one or more servers222 may select the subset of pre-determined questions. In otherembodiments, at least the first individual may be asked one or morequestions that are not pre-determined, such as one or more questionsthat may be dynamically generated in one or more of the servers 222.Such dynamically generated questions may be provided approximately inreal-time during the data-collection time interval.

At least the first individual may provide answers to the subset ofpre-determined questions one or more times during the data-collectiontime interval. The answers may be transmitted to one or more of theservers 222 and/or one or more of the optional databases 220. In someembodiments, the answers are transmitted at least in part using emailmessages and/or SMS messages, or only using email messages and/or SMSmessages. The email messages and/or SMS messages may be compressedand/or encrypted. One or more of the servers 222, in conjunction withinformation stored in one or more of the optional databases 220, mayanalyze the answers to determine one or more statistical relations, theranking of the variables, and/or to identify the one or more associationvariables. One or more of the servers 222 may revise the subset ofpre-determined questions that are provided and/or provide revisedconfiguration instructions, which may be non-executable, to at least oneof the computers 216 and devices 218 in order to modify the subset ofpre-determined questions for at least the first individual. In someembodiments, the configuration instructions may be provided at least inpart using email messages and/or SMS messages, or only using emailmessages and/or SMS messages. The email messages and/or SMS messages maybe compressed and/or encrypted. The configuration instructions may bedetermined in accordance with the answer history for at least a subsetof the one or more individuals, one or more groups of individuals,and/or default answers to the subset of pre-determined questions thatmay be stored in at least one of the optional databases 220. In someembodiments, the configuration instructions may be in accordance withthe analysis of the biological sample taken from the first individual(as discussed further below with reference to FIG. 19)

The second locations 212 may correspond to that of one or morehealthcare providers (such as a physician, nurse, chiropractor, and/oran associated staff member), one or more parents, one or more guardians,and/or one or more additional individuals that have the disease(henceforth referred to as a second individual). In some embodiments,one or more of the servers 222, in conjunction with information storedin one or more of the optional databases 220, may provide one or morerecommendations in accordance with the one or more determinedassociation variables to at least the second individual. In someembodiments, one or more of the servers 222, in conjunction withinformation stored in one or more of the optional databases 220, mayprovide the one or more recommendations in accordance with the one ormore determined association variables to at least the first individualat one or more of the first locations 210. The one or morerecommendations may be in the form of one or more reports or documents,including soft or hard copies. In some embodiments, the recommendationsmay be provided by transmitting a data stream including therecommendations and/or transmitting a data stream including instructionscorresponding to the recommendations (for example, the instructions maycorrespond to one or more web-pages) using techniques such as email,SMS, and/or by regular mail.

In other embodiments, at least the first individual may be asked one ormore questions that are provided by at least the second individual. Suchquestions from at least the second individual may be dynamicallygenerated and may be provided approximately in real-time during thedata-collection time interval. Answers to these and/or other dynamicallygenerated questions (such as those that may be provided by the one ormore servers 222) may be provided to at least the second individualand/or one or more servers 222 approximately in real-time or after atime delay. In some embodiment, at least the second individual mayprovide feedback and/or instructions to one or more of the servers 222that is based on the one or more recommendations. In some embodiments,the feedback and/or instructions may be used to revise the configurationinstructions. In some embodiments, the feedback and/or instructions maybe used to determine one or more additional pre-determined questionsthat are provided to at least the first individual.

While the system 200 has been shown with two computers 216 and twodevices 218 at the first locations 210, and two computers 216 at thesecond locations 212, there may be fewer or additional computers 216and/or devices 218. In addition, one or more individuals at the firstlocations 210 and/or the second locations 212 may share a computer, adevice, a set of computers, and/or a set of devices. Similarly, theremay be fewer or more servers 222 and/or optional databases 220. One ormore functions of one or more of the computers 216, devices 218, servers222, and/or optional databases 220 may be combined into a single item inthe system 200 and/or may be performed at one or more remote locationsin the system 200. One or more positions of one or more items in thesystem 200 may be changed. In some embodiments, the functions of the oneor more servers 222 and/or optional databases 220 may be performed inone or more of the computers 216 and/or one or more of the devices 218,for example, using one or more applications programs or modulesinstalled on one or more of the computers 216 or in one or moreremovable storage medium in one or more of the computers 216. In someembodiments, the one or more applications programs or modules installedon one or more of the computers 216 or in one or more removable storagemedium in one or more of the computers 216 may be dedicated orstand-alone applications that function without interactions with one ofthe servers 222 or only occasionally interact with one or more of theservers 222.

FIG. 3 is a block diagram illustrating an embodiment of a server orcomputer 300, such as one of the computers 216 and/or servers 222 inFIG. 2. The server or computer 300 includes one or more processing units(CPUs) 310 (which are each a means for computing), at least one networkor communications interface 322 for communicating with other computers,servers, devices, and/or databases, a memory device 324 (which is ameans for storage) with primary and secondary storage, at least oneoptional user interface 314, and one or more signal lines 312 forconnecting these components. The one or more processing units (CPUs) 310may support parallel processing and/or multi-threaded operation. Theoptional user interface 314 may have one or more displays 316, keyboards318, pointers 320 (such as a mouse), a touchpad (not shown), and/or avoice interface 308, including one or more speakers and/or microphones.The one or more displays 316 may include a touch screen (which maycombine the functionality of at least one of the keyboards 318 with atleast one of the displays 316). The one or more signal lines 312 mayconstitute one or more communication buses. The network orcommunications interface 322 may have a persistent communicationconnection.

The memory device 324 may include high speed random access memory and/ornon-volatile memory, including ROM, RAM, EPROM, EEPROM, FLASH, one ormore smart cards, one or more magnetic disc storage devices, and/or oneor more optical storage devices. The memory device 324 may store anoperating system 326, such as LINUX, UNIX, OS X, or WINDOWS, thatincludes procedures (or a set of instructions) for handling variousbasic system services for performing hardware dependent tasks. Thememory device 324 may also store procedures (or a set of instructions)in a network communications module 328. The communication procedures maybe used for communicating with one or more computers 216 (FIG. 2),servers 222 (FIG. 2), optional databases 220 (FIG. 2), and/or devices218 (FIG. 2). The communication procedures may include those for aparallel interface, a serial interface, an infrared interface,Bluetooth, Firewire (IEEE 1394A and/or IEEE 1394B), and/or a USBinterface (for example, USB-1 and/or USB-2 or High-Speed USB). Thecommunication procedures may include HyperText Transport Protocol (HTTP)to transport information using the Transmission ControlProtocol/Internet Protocol (TCP/IP), as well as a secure or encryptedversion of HTTP, such as Hypertext Transport Protocol over Secure SocketLayer (HTTPS), a Layer 2 Tunneling Protocol (L2TP), or another InternetProtocol Security, such as IPSEC.

The memory device 324 may also include the following elements, or asubset or superset of such elements, including a questionnaire module(or a set of instructions) 330, an encryption/decryption module (or aset of instructions) 340 (using, for example, pretty good privacy,symmetric encryption, and/or asymmetric encryption), a statisticalanalysis module (or a set of instructions) 342, a compound variablegenerator (or a set of instructions) 346, an optional signal processingmodule (or a set of instructions) 350, a report generator (or a set ofinstructions) 352 for formatting and providing recommendations andrelated information to at least one of the first individual and/or thesecond individual, pre-determined questions (or a set of instructions)354, pre-selected answers (or a set of instructions) 358, pattern ofoccurrence data 364 corresponding to one or more events and/or one ormore variables, association variable(s) 366, pre-determined sets ofassociation variables 368, and/or an optional location module (or a setof instructions) 370 for determining a location of one or more computers216 (FIG. 2) and/or one or more devices 218 (FIG. 2) (for example, usingan IP address, a global positioning system, and/or remote localizationcapability associated with a portable device such as a cellulartelephone).

The questionnaire module 330 may include a client communication module(or a set of instructions) 332 and/or a question pattern module (or aset of instructions) 336 for providing the subset of pre-determinedquestions to at least the first individual. The client communicationmodule 332 may include a web-page generator module (or a set ofinstructions) 334 that generates instructions corresponding to one ormore web pages, including HyperText Mark-up Language (HTML), extensibleMark-up Language (XML), Java, JavaScript, Perl, PHIP, and/or .NET. Thequestion pattern module 330 may include a configuration instructionsmodule (or a set of instructions) 338 for providing instructions thatselect the subset of pre-determined questions. The statistical analysismodule 342 may include a ranking module (or a set of instructions) 344.The compound variable generator 346 may include a threshold module (or aset of instructions) 348 for determining if one or more entries in thepattern of occurrence data 364 for at least one variable correspond to apresence or an absence. The pre-determined questions 354 may include oneor more question modules (or a set of instructions) 356. Thepre-selected answers 358 may include answer history 360 for one or moreindividuals and/or one or more groups of individuals, and/or defaultanswers (or a set of instructions) 362.

In some embodiments, the server or computer 300 may communicate with oneor more optional physiological monitors 372. Communication may be via acable (such as USB), infrared, Firewire and/or wireless (such as Wi-Fior Bluetooth). In an alternate embodiment, at least the first individualmay manually enter physiological data from the one or more optionalphysiological monitors 372 using one of the components in the optionaluser interface 314. In some embodiments, the physiological data may beentered using a binary search procedure corresponding to a series ofquestions, such as, “Is the physiological data value less than 0.5?,”“Is the physiological data value less than 0.25?,” “Is the physiologicaldata value greater than 0.375?,” and so on until a desired precision isobtained. (A similar binary search procedure may be used to answer oneor more pre-determined questions in the subset of pre-determinedquestions.) Communication with the one or more optional physiologicalmonitors 372 may be at discrete times or it may be continuous. The oneor more optional physiological monitors 372 may include anelectroencephalogram monitor (such as a Holter monitor), anelectrocardiogram monitor (such as a Holter monitor), anelectromyleogram monitor, an inflammatory response monitor, arespiratory monitor (such as the Air Watch II) of variables such as peakexpiratory flow and/or a forced expiratory volume in 1 second, a bloodglucose monitor, a blood pressure monitor, a thermometer, a vital signmonitor (such as those for pulse or respiration rate), a galvanometricresponse monitor, a psychomotor agitation monitor, and/or a reflex arcmonitor.

Instructions in the modules in the memory device 324 may be implementedin a high-level procedural language, an object-oriented programminglanguage, and/or in an assembly or machine language. The programminglanguage may be complied or interpreted, i.e, configurable or configuredto be executed by the one or more processing units 310. In addition, theserver or computer 300 may include fewer or additional executableprocedures, sub-modules, tables, and/or other data structures (notshown). In some embodiments, additional or different modules and datastructures may be used and some of the modules and/or data structureslisted above may not be used. In some embodiments, the functions of twoor more modules may be combined in a single module. In some embodiments,implementation of functionality of the server or computer 300 may beimplemented more in hardware and less in software, or less in hardwareand more in software, as is known in the art.

Although the server or computer 300 is illustrated as having a number ofdiscrete items, FIG. 3 is intended more as a functional description ofthe various features that may be present in the server or computer 300rather than as a structural schematic of the embodiments describedherein. In practice, and as recognized by those of ordinary skill in theart, the functions of the server or computer 300 may be distributed overa large number of servers or computers, with various groups of theservers or computers performing particular subsets of the functions.Items shown separately in the server or computer 300 may be combined,some items may be separated and/or additional items may be added. Theapparatuses and methods disclosed may be implemented in hardware and/orsoftware. In alternate embodiments, some or all of the functionality ofthe server or computer 300 may be implemented in one or more applicationspecific integrated circuits (ASICs) and/or one or more digital signalprocessors (DSPs).

FIG. 4 is a block diagram illustrating an embodiment of a computer 400,such as one of the computers 216 (FIG. 2). The memory device 324 mayinclude a browser module (or a set of instructions) 410 for renderingweb-page instructions, a transmission/receipt module (or a set ofinstructions) 414 for handling information such the subset ofpre-determined questions and corresponding answers, an optional voicerecognition module (or a set of instructions) 416, a display module (ora set of instructions) 418 for displaying the subset of pre-determinedquestions and/or the pre-selected answers to at least the firstindividual, configuration instructions 420, and/or a report(s) module(or a set of instructions) 422 for formatting and presentingrecommendations and related information to at least one of the firstindividual and the second individual. The browser module 410 may includeinstructions corresponding to one or more web pages 412. The computer400 may optionally perform at least a portion of the analysis, such asthe determining at least some of the statistical relationships. Thecomputer 400 may store pattern of occurrence data 364 corresponding toone or more events and/or one or more variables. The pattern ofoccurrence data 364 may be stored temporarily as the subset ofpre-determined questions are answered over at least a portion of thedata-collection time interval. For example, answers for a respective daymay be transmitted at night. In some embodiments, the pattern ofoccurrence data 364 may be communicated to one or more of the servers222 (FIG. 2) and/or optional databases 220 (FIG. 2) approximately inreal-time, for example, as respective pre-determined questions areanswered. In some embodiments, the computer 400 may communicate with oneor more optional physiological monitors 372.

Although the computer 400 is illustrated as having a number of discreteitems, FIG. 4 is intended more as a functional description of thevarious features that may be present in the server or computer 400rather than as a structural schematic of the embodiments describedherein. In practice, and as recognized by those of ordinary skill in theart, the functions of the server or computer 400 may be distributed overa large number of servers or computers, with various groups of theservers or computers performing particular subsets of the functions.Items shown separately in the computer 400 may be combined, some itemsmay be separated and/or additional items may be added. The apparatusesand methods disclosed may be implemented in hardware and/or software. Inalternate embodiments, some or all of the functionality of the server orcomputer 400 may be implemented in one or more ASICs and/or one or moreDSPs.

FIG. 5 is a block diagram illustrating an embodiment of a device 500,such as one of the devices 218 (FIG. 2). The device 500 may include acellular telephone, a personal digital assistant, a tablet computer, aBlackberry, a hand-held computer, and/or a combination of two or more ofthese items. In some embodiments, the device 500 may be an implantablemedical device that collects and communicates data for at least thefirst individual. The device 500 may include one or more dataprocessors, video processors and/or processors 510 (which are each ameans for computing), at least one communications interface 512 forcommunicating with other computers, servers, devices and/or databases, afirst memory device 516 (which is a means for storage) with primaryand/or secondary storage, a second optional memory device 524 that maybe removable (and which is also a means for storage), at least the oneuser interface 314, a sensor 508, and one or more signal lines 312 forconnecting these components. The one or more data processors, videoprocessors and/or processors 510 may support parallel processing and/ormulti-threaded operation. The user interface may have one or moredisplays 316, keyboards 318, pointers 320 (such as a mouse or a stylus),a touchpad (not shown), and/or a voice interface 308 including one ormore speakers and/or microphones. The one or more displays 316 mayinclude a touch screen (which may combine functionality of at least oneof the keyboards 318 with at least one of the displays 316). The one ormore signal lines 312 may constitute one or more communication buses.The communications interface 512 may include a radio transceiver 508 forconverting signals from baseband to one or more carrier bands and/orfrom one or more carrier bands to baseband. The communications interface512 may have a persistent communication connection. The device 500 mayinclude a power source 514, such as a battery or a rechargeable battery,for supplying power to one or more of these components. The sensor 508may be an imaging element, such as CCD array, for capturing one or moreimages (such as pictures).

The memory device 516 may include high speed random access memory and/ornon-volatile memory, including ROM, RAM, EPROM, EEPROM, FLASH, one ormore smart cards, one or more magnetic disc storage devices, and/or oneor more optical storage devices. The memory device 516 may store anembedded operating system 518, such as LINUX, UNIX, OS X, PALM, Simbianor WINDOWS, or a real-time operating system (such as VxWorks by WindRiver System, Inc.) suitable for use in industrial or commercialdevices. The operating system 518 may includes procedures (or a set ofinstructions) for handling various basic system services for performinghardware dependent tasks, including password, token and/or biometricsecurity authentication. The memory device 516 may also store procedures(or a set of instructions) in a communications module 520.

The communication procedures in the communications module 520 may beused for communicating with one or more computers 216 (FIG. 2), servers222 (FIG. 2), optional databases 220 (FIG. 2), and/or devices 218 (FIG.2). The communication procedures may include those for a parallelinterface, a serial interface, an infrared interface, Bluetooth,Firewire (IEEE 1394A and/or IEEE 1394B), and/or a USB interface (forexample, USB-1 and/or USB-2 or High-Speed USB). The communicationprocedures may include one or more protocols corresponding to a GlobalSystem for Mobile Telecommunications (GSM), Code Division MultipleAccess (CDMA), a Short Message Service (SMS), an Enhanced MessagingService (EMS), a Multi-media Message Service (MMS), a General PacketRadio Service (GPRS), a Wireless Application Protocol (WAP), instantmessaging, email, TCP/IP, and/or a voice over internet protocol (VoIP).Note that SMS supports communication of up to 160 characters using, forexample, text messaging. Email may utilize an email addresscorresponding to a subscriber's 10-digit telephone number, such as1234567890@messaging.carrier.com, where ‘carrier’ may be a cellulartelephone provider such as Cingular. EMS includes text formatting, andsupports communication of simple black and white images, as well assound tones. MMS supports communication of a wide variety of media fromtext to video.

The memory device 516 may also include the following elements, or asubset or superset of such elements, including the optional browsermodule (or a set of instructions) 410, the transmission/receipt module(or a set of instructions) 414, the encryption/decryption module (or aset of instructions) 340, the optional voice recognition module (or aset of instructions) 416, an optional voice replication module (or a setof instructions) 522 for asking at least some of the subset ofpre-determined questions using the voice interface 308, the displaymodule (or a set of instructions) 418, the configuration instructions420, the optional statistical analysis module (or a set of instructions)342, the optional compound variable generator (or a set of instructions)346, the optional signal processing module (or a set of instructions)350, the optional report(s) module (or a set of instructions) 422, theoptional answer history 360, and/or the optional pattern of occurrencedata 364.

The optional browser module 410 may include instructions correspondingto one or more web pages 412. The optional statistical analysis module342 may include the optional ranking module (or a set of instructions)344. The optional compound variable module 346 may include the optionalthreshold module (or a set of instructions) 348. The device 500 mayoptionally perform at least a portion of the analysis, such asdetermining at least some of the statistical relationships. Theencryption/decryption module 340 may include encryption/decryption thatis supported in the GSM and/or CDMA protocols. In some embodiments, theencryption/decryption module 340 may include a virtual private network(VPN) tunneling application.

The optional pattern of occurrence data 364 may be stored temporarily asthe subset of pre-determined questions are answered over at least aportion of the data-collection time interval. For example, answers for arespective day may be transmitted at night (when communication fees arelower). In some embodiments, the optional pattern of occurrence data 364may be communicated to one or more of the servers 222 (FIG. 2) and/oroptional databases 220 (FIG. 2) approximately in real-time, for example,as respective pre-determined questions are answered.

In an exemplary embodiment, the configuration instructions 420 and theanswers to the subset of predetermined questions may be communicatingusing one or more SMS text messages and/or email messages. In someembodiments, only SMS text messages and/or email messages are used. Theuse of SMS text messaging and/or email messaging may result in costsavings associated with establishing accounts and/or with thecommunication. Receipt of a respective SMS text message by an enddestination, such as one of the servers 222 (FIG. 2), may be confirmedusing a handshake message (such as another SMS message). Upon receipt ofsuch a handshake message, the device 500 may erase and/or deleteinformation that was transmitted in the original SMS text message (suchas one or more answers to the subset of pre-determined questions or atleast a portion of the optional pattern of occurrence data 364).

In some embodiments, at least the first individual may use the device500 to collect information that may be subsequently used to answer oneor more of the subset of pre-determined questions. For example, thesensor 508 may be used to take a picture of a menu, a table of contents,and/or one or more medicines consumed. Or an audio file listing itemsconsumed during a meal or snack may be recorded. The collectedinformation may be processing in the device 500, or in one or moreremote computers and/or one or more servers, using image processing,text recovery/identification, and/or speech recognition (using, forexample, the optional voice recognition module 416). In someembodiments, the device 500 may communicate with one or more optionalphysiological monitors 372.

In some embodiments, the device 500 may provide at least the firstindividual with at least one reminder (such as a reminder to take amedicine at a respective time) using one or more messages transmitted tothe device 500 and/or using one or more pre-stored messages in thedevice 500 that may be enabled by the configuration instructions 420. Atleast the one reminder may be provided using the voice replicationmodule 522 and the voice interface 308.

The optional memory device 524 may include one or more FLASH drives,ROMs, memory sticks, optical storage media (such as rewritable or ROMCDs and/or DVDs), smart cards, SIMS cards, secure digital (SD) cards(compatible with devices that use a PALM embedded operating system),multimedia cards (MMCs), magnetic disc storage devices (such as discdrives), and/or magnetic media (such as floppy discs). The optionalmemory device 524 may also include the following elements, or a subsetor superset of such elements, including the pre-determined questions (ora set of instructions) 354, the pre-selected answers 358 (or a set ofinstructions), and/or the optional account information 526. Thepre-determined questions 354 may include the one or more questionmodules (or a set of instructions) 356. The pre-selected answers 358 mayinclude optional default answers (or a set of instructions) 362. Theoptional account information 526 may include at least one carrieraccount number (for an Internet service provider, a cellular telephoneprovider, and/or a wireless services provider) and/or at least onetelephone number that may enable at least the first individual toreceive the configuration instructions 420 and to transmit the answersto the subset of pre-determined questions. The optional accountinformation 526 may allow at least the first individual to communicatewith one or more providers of services that determine one or moreassociation variables.

In some embodiments, at least first individual is provided with theoptional memory device 524, which may be installed in the device 500.One or more additional optional memory devices may also be provided toat least the first individual at later times during the data-collectiontime interval. The one or more additional optional memory devices mayinclude revised pre-determined questions 354 and/or revised pre-selectedanswers 358.

In some embodiments, the optional memory device 524 is a SIMS card, anSD card, and/or a memory card and the device 500 includes a cellulartelephone. By including the optional account information 526,communication using at least the one carrier account number and/or atleast the one telephone number may avoid the so-called SIMS card lockthat prevents modification of such information in a cellular telephonethat is issued by a cellular telephone provider to a subscriber. Thismay allow one or more modules or applications to be installed and/orexecuted on the cellular telephone independently of the cellulartelephone provider. Address book and/or additional telephone numbers(such as a list of frequently used telephone numbers) on an existingSIMS card, SD card, and/or memory card for at least the first individualmay be copied on to the optional memory device 524. Alternatively, atleast the first individual may at least temporarily provide the existingSIMS card, SD card, and/or memory card, which may allow the address bookand/or the additional telephone numbers to be copied on to a newoptional memory device 524 that may be provided to at least the firstindividual.

In some embodiments, the pre-determined questions 354 and/or thepre-selected answers 358 may be copied on to the optional memory device524 or the existing SIMS card, SD card, and/or memory card in a cellulartelephone. Service, such as collecting the information, may be providedin conjunction with or separately from one or more cellular telephoneservice providers. In some embodiments, revised pre-determined questions354 and/or revised pre-selected answers 358 may be transmitted to andstored on the optional memory device 524 on one or more occasions duringthe data-collection time interval.

In some embodiments, at least the first individual owns or rents thedevice 500, thereby reducing a cost associated with collectinginformation, as well as a cost and complexity associated with supportingand maintaining hardware in the field. In some embodiments, at least thefirst individual is provided, either temporarily or permanently, withthe device 500. Furthermore, in some embodiments memory 524 is includedin the device 500 when it is provided to the first individual. Thus, thedevice 500 is pre-configured or pre-loaded with the pre-determinedquestions 354.

The modules and/or some components in the memory device 516 may bearranged in a protocol stack, including a physical layer, a link layer,a network layer, a transport layer, and/or an application layer. In analternate embodiment, the protocol stack may include the network layer,the transport layer, a security layer, a session transaction layer,and/or the application layer. Instructions in the modules in the memorydevice 516 may be implemented in a high-level procedural language, anobject-oriented programming language, and/or in an assembly or machinelanguage. The programming language may be complied or interpreted, i.e,configurable or configured to be executed by one or more processors 510.In addition, the device 500 may include fewer or additional executableprocedures, sub-modules, tables and other data structures (not shown).In some embodiments, additional or different modules and data structuresmay be used and some of the modules and/or data structures listed abovemay not be used. In some embodiments, the functions of two or moremodules may be combined in a single module. In some embodiments,implementation of functionality of the device 500 may be implementedmore in hardware and less in software, or less in hardware and more insoftware, as is known in the art.

Although the device 500 is illustrated as having a number of discreteitems, FIG. 5 is intended more as a functional description of thevarious features which may be present in the device 500 rather than as astructural schematic of the embodiments described herein. In practice,and as recognized by those of ordinary skill in the art, the functionsof the device 500 may be distributed over a large number of servers orcomputers, with various groups of the servers or computers performingparticular subsets of the functions. Items shown separately in thedevice 500 may be combined, some items may be separated and/oradditional items may be added. One or more items or modules in thememory device 516, such as the configuration instructions 420, may bestored in the optional memory device 524 and vice versa. The apparatusesand methods disclosed may be implemented in hardware and/or software. Inalternate embodiments, some or all of the functionality of the device500 may be implemented in one or more ASICs and/or one or more DSPs.

Attention is now directed towards embodiments of the questionnaire andformats, including graphical user interfaces, for displaying informationcontained in embodiments of the questionnaire. As noted previously, thesubset of pre-determined questions may be provided (for example,displayed) to at least the first individual along with respectivepre-selected answers for each question in at least a plurality of thepre-determined questions in the subset of pre-determined questions. Inthis way, answering a respective pre-determined question may involveselection if a respective answer to the respective pre-determinedquestion is different than a respective pre-selected answer. Thepre-selected answers may be selected in accordance with an answerhistory, such as the answer history 360 (FIG. 3), and/or defaultanswers, such as the default answers 362 (FIG. 3). FIGS. 6A-6Billustrate embodiments of user interfaces that include pre-determinedquestions and pre-selected answers. FIGS. 6C-6D illustrate embodimentsof user interfaces for receiving information corresponding to foods andbeverages that were consumed by the first individual during a meal, asnack, and/or during a time interval.

FIG. 6A is a block diagram illustrating an embodiment of a questionnaire600, such as a questionnaire module (which may contain related questionsin a category of questions). Questionnaire modules are discussed furtherbelow with reference to FIGS. 8A and 8B. In the questionnaire 600,several primary questions 610, including pre-selected answers 616 andalternate answers 618, are displayed in a window 608. The window may bea graphical user interface, such as a dialog box or window, and it maybe displayed on a display, such as the display 316 (FIG. 4). A left edgeof at least a plurality of the primary questions 610 may be aligned withalignment 612-1 such that the primary questions 610 are arranged in acolumn. A left edge of at least a plurality of the pre-selected answers616 may be aligned with alignment 612-3 such that the pre-selectedanswers 616 are arranged in a column. A left edge of at least aplurality of the alternate answers 618 may be aligned with alignment612-4 such that the alternate answers 618 are arranged in a column. Inother embodiments, a right-edge, a center or another position in theprimary questions 610, the pre-selected answers 616 and/or the alternateanswers 618 may be used for purposes of alignment.

If the pre-selected answers 616 are correct, at least the firstindividual may select a next 624 icon at the bottom of the window 608(for example, by positioning a cursor over the accept 624 icon and leftclicking on a mouse, or by touching or making a gesture on a touchscreen) to accept the pre-selected answers 616 and request anotherwindow (unless the questionnaire 600 is completed) with additionalpre-determined questions and/or pre-selected answers. Selection of oneor more of the alternate answers 618, such as alternate answer 618-1,may occur if a corresponding pre-selected answer 616-1 is not thecorrect answer for a corresponding primary question, such as primaryquestion 610-1.

If one or more of the alternate answers 618 are selected, secondaryquestions 620 may be displayed and/or enabled (i.e., at least the firstindividual may be able to modify the answer to a secondary question,such as secondary question 620-1). In some embodiments, when analternate answer, such as alternate answer 618-1 is selected, adisplayed color of one or more of one or more previously displayedsecondary questions 620 may be changed, for example, from grey to blackor from white to black. Note that some primary questions 610 may nothave one or more associated secondary questions 620 associated withthem. The secondary questions 620 may dependent on or may be conditionalon the answers to corresponding primary questions 610. A left edge of atleast a plurality of the secondary questions 620 may be aligned withalignment 612-2 such that the secondary questions 620 are arranged in acolumn. In other embodiments, a right-edge, a center or another positionin the secondary questions 620 may be used for purposes of alignment.The alignment 612-2 may be offset 614 from the alignment 612-1.

In the questionnaire 600, the primary questions 610 may be categoricalor discrete questions, i.e., having answers such as ‘yes’ or ‘no’. Insome embodiments, the pre-selected answers 616 may include both ‘yes’and ‘no’ responses. As a consequence, the pre-selected answers 616 mayalternate between ‘yes’ and ‘no’ for different primary questions 610. Inan alternate embodiment, ‘yes’ answers may be arranged in a first columnand ‘no’ answers may be arranged in a second column, such that aposition for the pre-selected answers 616 and the alternate answers 618may varying between the first column and the second column depending onthe primary questions 610. In the questionnaire 600, the secondaryquestions 620 may include categorical questions as well as one or moreordered categorical secondary question 620-2. (A left edge of thealternate answer 618-4 may be aligned with alignment 612-5. In otherembodiments, a right-edge, a center or another position in the alternateanswer 618-4 may be used for purposes of alignment.) In an orderedcategorical question, there is an ordering between values (such asanswers of ‘small’, ‘medium,’ or ‘large’) but a scale or metric valuemay vary (a difference between ‘medium’ and ‘small’ may be differentthan a difference between ‘large’ and ‘medium’). In some embodiments, anordered categorical question may have a pre-selected answer, such aspre-selected answer 616-3, that is in a different column than otherpre-selected answers 616. This may be useful when the ordered categoriesare time intervals (such as 3 and/or 6 hours, or morning and/orafternoon, etc.), and the pre-selected answer is not the left-mostordered category. Rather than rearranging the ordered categories, thepre-selected answer 616-3 may be in a different column.

In some embodiments, the primary questions 610 and the secondaryquestions 620 may include categorical, ordered categorical and/orquantitative questions. Quantitative questions have answers that arecontinuous variables. Answers to quantitative questions may bepartitioned, for example using one or more thresholds or thresholdvalues, to generate categorical or ordered categorical answers. In someembodiments, answers to one or more quantitative questions may be bandlimited prior to partitioning to reduce or eliminate aliasing.Categorical or ordered categorical answers may be converted intocontinuous answers using interpolation (such as minimum bandwidthinterpolation), subject to the limitations associated with the Nyquistsampling criterion.

In the questionnaire 600, the window 608 may include one or more helpicons 632, a home icon 622 to return to a master page in thequestionnaire 600, a jump icon 626 to save answers and skip to anotherwindow, an exit icon 628 to save answers and exit the questionnaire 600.While the questionnaire 600 includes three primary questions 610 andthree secondary questions 620, there may be fewer or more of either typeof question. In some embodiments, a respective question may have one ormore answers. In some embodiments, one or more additional questions maybe included on some occasions when the questionnaire 600 is displayed inorder to keep the questionnaire from becoming predictable, and thus lessinteresting, to the first individual. The one or more additionalquestions may be displayed with a different color and/or font than theother questions in the questionnaire 600.

FIG. 6B is a block diagram illustrating an embodiment of a questionnaire640. Selection of a respective alternate answer, such as alternateanswer 618-6 (FIG. 6A), to one of the primary questions 610 (FIG. 6A) orsecondary questions 620 (FIG. 6A) may lead to window 652 beingdisplayed. The window 652 may include one or more help icons 664, andone or more items 654 with one or more quantities 658 and units 660(henceforth collectively referred to as entries) during correspondingtime intervals 656. At least the first individual may accept the entriesusing an accept icon 662 after making modifications (if any).Modifications may be made to one or more entries by positioning a cursorover an entry and manually typing one or more new values and/or by leftclicking on the mouse with the cursor over the entry and selecting a newvalue from a list box (a static object in the window 652) that appearswhen the mouse is positioned over it and/or a content-dependent list ormenu (a dynamic object) that may appear as a separate window when themouse is positioned over it. A list box and/or a content-dependent listor menu may include related objects and/or items, such as thosecorresponding to a category of items. In some embodiments, the entriesin the window 652 may blink to indicate that at least the firstindividual may modify one or more of them. In an exemplary embodiment,the items 654 may include pharmacological agents, prescription drugs,vitamins, herbs, supplements and/or recreational or illicit drugs(henceforth referred to as pharmacological agents). The quantities 658and units 660 may correspond to dosages. The time intervals 656 may be afraction of a day, such as approximately 1, 2, 3, 4, 6, 8, and/or 12hours. Thus, 50 mg of a drug taken in the morning (such as between 6 AMand 11.59 AM), may correspond to a quantity 658-1 of ‘50’ and a unit660-1 of ‘mg’. The entries in the window 652 may be pre-selected basedon the answer history, default answers, and/or answers to one or morequestions in the optional initial survey, which may include informationabout pharmacological agents (such as times taken and dosages) that areprescribed to and/or used by at least the first individual. The optionalinitial survey may be conducted prior to or at the beginning of thedata-collection time interval during which the subset of pre-determinedquestions corresponding to the questionnaire are asked.

FIG. 6C is a block diagram illustrating an embodiment 670 of a userinterface 680, such as a graphical user interface. The user interface680 includes a first window 682-1 to receive and display informationcorresponding to a first item consumed by an individual during a firsttime interval. The first time interval may correspond to a snack or ameal, such as lunch. In some embodiments, snacks eaten between meals maybe included in the nearest previous meal and/or the nearest subsequentmeal. For example, a snack after dinner and before breakfast may beincluded with the entries for dinner. A user, such as the firstindividual, may provide information 690-1 that is displayed in a field684-1. In an exemplary embodiment, the first individual provides theinformation 690-1 using an entry device, such as a keyboard, or verbally(in which case voice recognition module 416 in FIG. 4 may be used). Theinformation 690-1 may correspond to one or more foods and/or one or morebeverages consumed by the first individual. The information 696-1 mayinclude brand information, such as a manufacturer or a restaurant,corresponding to the one or more foods and/or the one or more beveragesconsumed by the first individual.

One or more selectable items 692-1 may be determined and displayed inaccordance with at least a portion of the information 690-1, such as oneor more characters in the information 690-1. In some embodiments, theone or more selectable items 692-1 may be determined using a searchvector. The search vector may include one or more synonyms for one ormore characters in at least the portion of the information 690-1.Furthermore, the search vector may include one or more alternativespellings for one or more characters in at least the portion of theinformation 690-1. The search vector may also include a reordering ofand/or may exclude one or more characters in at least the portion of theinformation 690-1.

In some embodiments, the one or more selectable items 692-1 may bedetermined in accordance with a match score between the search vectorand one or more of the one or more selectable items 692-1. The matchscore may correspond to a weighted summation of terms. A respective termand a respective weight in the summation may correspond to agreementbetween an item in the search vector (such as a synonym for one or morecharacters in at least the portion of the information 690-1) and a foodor beverage in a list of foods and/or beverages. In some embodiments,the one or more selectable items 692-1 may be determined in accordancewith natural language processing.

The first individual may select one of the select items 692-1, forexample, by positioning a mouse over a respective item and clicking theleft button, or by touching or making a gesture on a touch screen. Arespective item that is selected or provided (such as the information690-1) may be displayed along with other items 691-1 that have beenpreviously selected and/or provided. For example, the respective itemmay be at the top or the bottom of the other items 691-1.

The items 691-2 may also be displayed along with items 691-1. The items691-2 may correspond to food amounts or quantities for the correspondingitems 691-1. The items 691-2 may include one or more default quantities,such as a normal amount or a usual amount consumed by the firstindividual. The default quantity for each of the selected items 691-1may be in accordance with the answer history. The first individual maychange a respective default quantity, for example by positioning a mouseover a respective item and clicking the right button, or by touching ormaking a gesture on a touch screen. In response to such an action by thefirst individual, a window 682-3 may be displayed. The window 682-3 mayinclude other categorical answers for food quantities, such as smalleramount (‘less than usual’) and/or larger amount (‘more than usual’). Thefirst individual may select one of these quantities using a mouse, or bytouching or making a gesture on a touch screen.

The user interface 680 may also include a second window 682-2. Thesecond window 682-2 may include one or more selectable items 692-2 thatcorrespond to one or more foods and/or beverages consumed by the firstindividual during a second time interval. In some embodiments, thesecond time interval precedes and/or at least partially overlaps thefirst time interval. In some embodiments, the second time intervalincludes the first time interval. In an exemplary embodiment, one ormore selectable items 692-2 may correspond to foods and/or beveragesconsumed by the first individual during previous meals and/or snacks.The one or more selectable items 692-2 may include the most common foods(for example, a top-10 list) consumed by at least the first individual,for example, during a given meal. The first individual may select one ormore of the one or more selectable items 692-2 using a mouse, or bytouching or making a gesture on a touch screen. Selected items in theone or more selectable items 692-2 may be displayed in the first window682-1 along with the items 691-1.

The first individual may correct errors in the items 691-1 (for example,if a respective item was selected by accident) by clicking on therespective item using a mouse or by touching the respective item andmaintaining contact (in embodiments with a touch screen) and draggingthe respective item to a trash icon 688. When the respective item isremoved from the items 691-1, the corresponding item in the items 691-2is no longer displayed.

In some embodiments, the first individual may define a compound food byselecting a compound food icon or button 686 (for example, using amouse, or by touching or making a gesture on a touch screen). A compoundfood item, for example, a dish such as lasagna, includes one or moreingredients and may be displayed along with the other selectable items692-1 and/or 692-2. A user interface for defining such a compound foodis described below with reference to FIG. 6D.

The user interface 680 may be implemented as a method and/or as acomputer-program product that is to be used in conjunction with acomputer system and/or a device. In embodiments where windows in theuser interface 680 are provided in one or more web pages, instructionsincluded with the one or more web pages may allow the first window 682-1to be updated without blinking the displayed user interface 680, i.e.,without transmitting revised web page instructions from a remote serveror computer, such as the server computer 300 (FIG. 3). The userinterface 680 may include one or more help icons (not shown), the homeicon 622, the next icon 624, the jump icon 626 and/or the exit icon 628.In some embodiments, the user interface 680 may include additional orfewer elements, such as windows 682, two or more elements may becombined into a single element, and/or a relative position of one ormore elements may be changed.

FIG. 6D is a block diagram illustrating an embodiment of a userinterface 694, such as a graphical user interface, for receiving two ormore ingredients in a respective compound food. A window 682-4 includesinformation 690-3 corresponding to a name for the respective compoundfood. The information 690-3 may have been provided by the firstindividual while interacting with the user interface 680 (FIG. 6C). Thewindow 682-4 may include a field 684-2 that displays information 690-2provided by the first individual. In an exemplary embodiment, the firstindividual provides the information 690-2 using an entry device, such asa keyboard, or verbally (in which case voice recognition module 416 inFIG. 4 may be used). The information 690-2 may correspond to one or moreingredients in the respective compound food. One or more selectableitems 692-3 may be displayed in accordance with at least a portion ofthe information 690-2, such as one or more characters in the information690-2, in a fashion similar to that described above for the userinterface 680 (FIG. 6C).

The first individual may select one of the select items 692-3, forexample by positioning a mouse over a respective item and clicking theleft button, or by touching or making a gesture on a touch screen. Arespective item that is selected or provided (such as the information690-2) may be displayed along with other items 691-3 that have beenpreviously selected and/or provided.

The first individual may correct errors in the items 691-3 (for example,if a respective item that selected by accident) by clicking on therespective item using a mouse or by touching the respective item andmaintaining contact (in embodiments with a touch screen) and draggingthe respective item to the trash icon 688.

The user interface 694 may be implemented as a method and/or as acomputer-program product that is to be used in conjunction with acomputer system and/or a device. In embodiments where windows in theuser interface 694 are provided in one or more web pages, instructionsincluded with the one or more web pages may allow the window 682-4 to beupdated without blinking the displayed user interface 694, i.e., withouttransmitting revised web page instructions from a remote server orcomputer, such as the server computer 300 (FIG. 3). The user interface694 may include an accept icon 696 and a cancel icon 698. Activatingand/or selecting the cancel icon 698 may close the user interface 694without defining a compound food. Activating and/or selecting the accepticon 696 may close the user interface 694 and define a compound food.Compound foods may be highlighted (for example, using a different fontand/or a different color than other text) and/or shown in bold in theuser interface 680 (FIG. 6C).

In some embodiments, the user interface 694 may include additional orfewer elements, a relative position of one or more elements may bechanged, and/or two or more elements may be combined into a singleelement.

A better understanding of the questionnaire and the determining of oneor more association variables (described further below with reference toFIGS. 9-14) may be provided by considering application to a class ofproblems, such as those associated with one or more diseases. Migrainesare used as an illustrative example. In this example, at least the firstindividual may be a migraine patient. In some embodiments, migraines mayinclude probable migraine, also referred to as migrainous, in whichpatients exhibit migraines minus one migraine symptom (which arediscussed below).

Migraine is a neurovascular disorder characterized by a family ofsymptoms that often include severe, recurring headache usually onone-side of the head. Migraine attacks are debilitating and have aduration that may last from several hours to days. During attacks, manypatients also exhibit sensitivity to environmental stimuli, such aslight and sound, and/or experience nausea or vomiting. Somecharacteristics of migraines, with and without aura, are summarized inTables I and II. Migraines typically follow a cycle, including aninitial or prodrome phase during which premonitory symptoms (discussedfurther below with reference to FIG. 8A) may be present, an aura phase(for patients that have migraine with aura) during which visualdisturbances may be present, a resolution or recovery phase, and anormal (i.e., non-migraine) phase.

TABLE I Some characteristics of migraine without aura. A. Headacheattacks lasting 4-72 hours (untreated or unsuccessfully treated). B.Headache has at least two of the following characteristics: 1.Unilateral location; 2. Moderate or severe pain intensity; 3. Pulsatingquality; 4. Aggravated by or causing avoidance of routine physicalactivity (for example, walking or climbing stairs). C. During headacheat least one of the following: 1. Nausea and/or vomiting; 2. Lightsensitivity (photophobia) and sound sensitivity (phonophobia).

TABLE II Some characteristics of migraine with aura. A. Aura consistingof at least one of the following, but no motor weakness: 1. Fullyreversible visual symptoms including positive features (for example,flickering lights, spots or lines) and/or negative features (such as,loss of vision); 2. Fully reversible sensory symptoms including positivefeatures (such as, pins and needles) and/or negative features (such as,numbness); 3. Fully reversible dysphasic speech disturbance. B. At leasttwo of the following: 1. Homonymous visual symptoms and/or unilateralsensory symptoms; 2. At least one aura symptom develops gradually over≧5 minutes and/or different aura symptoms occur in succession over ≧5minutes; 3. Each symptom lasts ≧5 and ≦60 minutes. C. Headachefulfilling criteria A-C for migraine without aura (Table I) beginsduring the aura or follows aura within 60 minutes.

The medical approach to managing migraine headaches is typicallythree-pronged, including acute therapy, preventive therapy, andidentification and avoidance of migraine triggers. Acute therapyincludes administering acute or prophylactic pharmacological agents,such as painkillers or analgesics, (for example, aspirin, acetaminophenor naproxen), ergotamine, dihydroergotamine, and/or a new class ofmedications known as “triptans” (selective serotonin 5-hydroxytryptamineor 5-HT receptor agonists, such as imitrex and maxalt), which aremigraine-specific medications that may treat the entire migrainecomplex, relieving the head pain, nausea, vomiting, and associated lightand sound sensitivity, typically within 1-2 hours. Most patients withmigraine are prescribed one or more forms of acute therapy.

Preventive therapy includes pharmacological agents or medications takenon a daily basis to reduce migraine headache frequency (a number ofheadaches during a time period), duration and/or severity (for example,a rating of headache pain by a patient such as the first individual).These pharmacological agents may be taken whether a migraine headache ispresent or not. Prevention strategies are typically employed forpatients who suffer from one or more migraine headaches per week. Only aminority of patients require this form of therapy.

Identification and avoidance of migraine triggers is typically amainstay in the treatment of patients suffering from migraine. Ifpatients successfully avoid their migraine triggers, migraine headachefrequency, duration and/or severity may be improved. (Note that somemigraine triggers, such as certain hormones, may be intrinsic orinternal to the patient. As such, the patient may still have spontaneousmigraine attacks even if he or she successfully avoids his or herdominant migraine triggers.) Identifying migraine triggers, however,remains challenging and is often a source of frustration for patientsand healthcare providers. This is partly an outgrowth of the apparentcomplexity of migraine triggers. A myriad of probable or putativemigraine triggers are thought to exist. It has been hypothesized thatthe migraine triggers may vary significantly from one patient toanother, may vary within a respective patient (as discussed below withreference to FIG. 7, the respective patient's sensitivity threshold fora respective trigger may vary as a function of time), may depend on aquantity of exposure, and/or may depend on exposure to two or moretriggers in close temporal proximity, i.e., during a time interval.

In addition, current approaches for screening for migraine triggers maypose challenges. Typically, patients are given paper diaries and areasked to list what they think may have triggered an attack on arespective day. This approach may rely on the patients' recall ofevents, as diaries are often filled in days after a migraine attack, andmay therefore miss an exposure to migraine triggers. Patients may alsoassign a cause when one may not exist, or patients may assign blame toan incorrect variable(s). The apparent complexity of migraine triggersmay compound these difficulties.

FIG. 7 is a block diagram illustrating an embodiment 700 of migrainetriggers and sensitivity thresholds. A plurality of variables 712,corresponding to migraine triggers, each having a length correspondingto an amount of exposure 714 during a time interval are illustrated.Sensitivity thresholds 710 illustrate several effective sensitivitiesfor at least the first individual. At any given time, one or more of thesensitivity thresholds 710 may be operative for one or more of thevariables 712. The sensitivity thresholds 710 may vary as a function oftime. Such variation may occur slowly, for example, over a period ofmonths or even years. Variables 712 that exceed one of the sensitivitythresholds 710 corresponding to a current sensitivity for at least thefirst individual, such as variable B 712-2 and sensitivity threshold710-2, may trigger a migraine attack. Alternatively, combinations ofvariables 712, such as variable C 712-3 and variable F 712-6, may exceeda sensitivity threshold 710-3 and trigger a migraine. The combinationsmay be cumulative, may correspond to variables 712 that occur in closetemporal proximity, may correspond to respective temporal sequences ofvariables 712, and/or may correspond to respective ordered temporalsequences of variables 712 during at least a portion of thedata-collection time interval. While embodiment 700 illustrates sevenvariables 712, in some embodiments there may be fewer or more variables712.

Other intricacies associated with migraines are so-called rebound andrecurrence headaches. While analgesics are designed to relieve pain, ifsuch pharmacological agents (both prescription and nonprescription) areoverused (repetitive and chronic use), they can actually causeheadaches. This is known as analgesic rebound headache (ARH) or “reboundheadache.” Headache sufferers taking analgesic medications every day, oreven as infrequently as two times a week, may find that they must takeever-increasing dosages to achieve relief. With continued overuse themedication becomes less and less effective, with pain-free periodsbetween headaches becoming shorter and shorter. The result can be aself-sustaining cycle of increasing pain and medication.

Recurrences headaches are associated with headaches returning after apain-free period following treatment with one or more medicines. Inessence, the migraine attack “outlasts” the treatment, so the headachereturns when the medication wears off. Recurrence is commonly seenfollowing treatment with a triptan. For example, a headache resolveswithin one to two hours after taking a triptan, only to return fullblown (i.e., with full severity) within 24 hours. In some embodiments, arecurrence headache may be defined as any headache occurring after aheadache-free state at 2 hours and within 12 hours after intake of anacute pharmacological agent. In some embodiments, a recurrence headachemay be defined as any headache occurring after a headache-free state at2 hours and within 24 hours after intake of an acute pharmacologicalagent. A recurrence headache may have different characteristics ofintensity, severity and/or associated features than an original headacheepisode during a migraine attack. In some cases, a recurrence headachemay be of migraine or tension-type. (Some characteristics of tensionheadaches are summarized in Table III.)

TABLE III Some characteristics of tension headaches. A. At least 2 ofthe following 4 headache features: 1. Bilateral location; 2.Pressing/tightening quality, 3. Mild or moderate intensity; 4. Notaggravated by routine physical activity. B. Both of the following: 1. Nonausea or vomiting 2. Not more than one of light or sound sensitivity.C. Duration lasting from 30 minutes to 7 days.

In the context of the disclosed embodiments, the one or more events maybe one or more migraines, and the one or more temporal onsetscorresponding to the one or more events may be a respective onset timeand/or a respective onset time interval for one or more migraines. Onsettimes for migraines may be determined in accordance with one or morepremonitory symptoms (discussed further below with reference to FIG. 8A)that may be experienced by at least the first individual during theprodrome phase of a migraine attack, one or more migraine symptomsduring the aura phase of a migraine attack, one or more migrainesymptoms during the headache phase of a migraine attack, and/or an onsetof head pain as indicated by at least the first individual.

In some embodiments, the one or more physiological monitors 372 (FIG. 3)may, at least in part, determine one or more onset times for migraines.Since migraines impact the hypothalamus, with consequences for theendocrine system, the limbic system and the autonomic nervous system, avariety of physiological changes may be observable in one or moremigraine patients. These physiological changes may include changes in acircadian rhythm, changes in one or more vital signs (such as pulse,respiration, systolic blood pressure, and/or diastolic blood pressure),hormonal changes, emotional changes, changes in a pulse pressure(defined as a difference of the systolic blood pressure and thediastolic blood pressure), changes in skin electrical or thermalconductivity (such as perspiration), and/or changes in at least onereflex arc. The physiological changes may be bilateral or unilateral. Inan exemplary embodiment, the pulse pressure may increase or decrease(relative to an average pulse pressure during the normal phase) by 1%,3%, 5% or more than 10% during the prodrome phase. In some embodiments,the one or more physiological monitors 372 (FIG. 3) may determine apresence of cutaneous allodynia or ‘skin pain’ (such as a sensitive orpainful scalp), a condition associated with central sensitization, whichis indicative of a deeply entrench migraine attack. In some embodiments,the one or more physiological monitors 372 (FIG. 3) may provide a metricof chronic disease regulation, for example, a frequency, a durationand/or a severity of migraines.

The pre-determined questions in the questionnaire, such as thequestionnaire 600 (FIG. 6A), may correspond to patterns of occurrence(including presence and absence information) of the set of variablesthat are potential migraine triggers. The one or more associationvariables may correspond to one or more migraine triggers, and/or one ormore probable or putative migraine triggers. The one or more associationvariables may be patient-specific, may occur in one or more groups ofmigraine patients, and/or may occur in at least a plurality of migrainepatients. The one or more association variables may at least in partinduce a migraine in at least the first individual if at least the firstindividual is exposed to one or more of the association variables. Insome embodiments, the one or more association variables may at least inpart induce a migraine in at least the first individual if at least thefirst individual is exposed to an amount of the one or more of theassociation variables that is greater than a pre-define value during atime interval. In some embodiments, the one or more associationvariables may be the dominant migraine triggers, such as those migrainetriggers associated with 10%, 25%, 33%, 50%, or more of the migraineattacks, for at least the first individual.

Variables that may be migraine triggers may include weather changes,allergens, compounds containing phenols (also referred to as phenoliccompounds), pollution, hormonal fluctuations (such as during themenstrual cycle, pregnancy, post partum, and/or menopause), trauma,illness, hypoglycemia, sensory stimuli (such as lights, sounds, and/orsmells), physical exertion, sexual activity, motion, travel, sleeppatterns (when and/or how much sleep), intense emotion, withdrawal ofintense emotion, stress, withdrawal of stress, certain pharmacologicalagents (such as MAO inhibitors, oral contraceptives, estrogenreplacement therapy, recreational drugs, and/or tobacco products),dietary patterns (when food is consumed), and/or diet (what and/or howmuch is consumed). Dietary migraine triggers may include alcohol (forexample, wine), sugar substitutes (such as Aspartame), caffeine(including caffeine withdrawal), food additives (such as monosodiumglutamate or MSG), processed meats, one or more fruits, one or morevegetables, one or more spices, one or more nuts, fermented food (suchas vinegar), foods containing amounts of certain amino acids (such astyramine) that exceed one or more quantity thresholds, foods containingamounts of nitrites and/or nitrates that exceed a first quantitythreshold, foods containing amounts of sulfites that exceed a secondquantity threshold, and/or foods containing amounts of tannins thatexceed a third quantity threshold. For example, dietary migrainetriggers may include blue cheese, oranges, carrots, vinegar andcaffeine.

Attention is now directed towards application of the embodiments of thequestionnaire for collecting information associated with migraines, suchas variables corresponding to potential or putative migraine triggers.It should be understood, however, that the description applies tonumerous applications and embodiments, including non-medicalapplications.

Prior to or at the beginning of the data-collection time interval, atleast the first individual may answer one or more questions in theoptional initial survey. In some embodiments, the questions may be basedon at least the first individual's medical history. The optional initialsurvey may confirm that at least the first individual meets anyapplicable entry criteria, determine one or more questionnaire modules(discussed further below with reference to FIG. 8A) that may be relevantfor at least the first individual, and collect initial information, suchany pharmacological agents that at least the first individual takes on aregular basis (for example, daily). In the case of migraines, entrycriteria may include determining that the disease in a respectivepatient, such as at least the first individual, is sufficiently wellcontrolled that migraine attacks are not occurring too often (such asevery day) or too infrequently (such as once a year) to precludedetermination of one or more migraine triggers. For migraines,pharmacological agents may include one or more acute therapies and/orone or more preventive therapies. The pharmacological agents may includeother medicines (prescription and/or non-prescription), vitamins, herbs,supplements, and/or recreational drugs that at least the firstindividual takes on a regular basis. The initial information may includequantities and/or times when one or more of the pharmacological agentsare used. As discussed further below with reference to FIG. 19, in someembodiments a biological sample from at least the first individual maybe analyzed prior to or at the beginning of the data-collection timeinterval. Furthermore, in some embodiments a psychological profile isdetermined during the initial survey.

As noted above, pre-determined-questions in the questionnaire may begrouped into questionnaire modules. FIG. 8A is a block diagramillustrating an embodiment of a questionnaire data structure 800including multiple pre-determined questions (such as the pre-determinedquestions 354 in FIG. 3) that are arranged in multiple questionnairemodules (such as the questionnaire modules 356 in FIG. 3). Acorresponding questionnaire data structure is discussed below withreference to FIG. 22. The questionnaire data structure 800 may includesleep pattern questions 810 (including questions related to sleep apneaand/or insomnia), dietary questions 812 (such as dietary patterns anddiet), behavioral questions 814 (such as hormonal fluctuations, physicalexertion, sexual activity, motion, travel, exposure to intense emotion,withdrawal of intense emotion, exposure to stress, withdrawal of stress,and/or a use of tobacco products), environmental questions 816 (such asexposure to sensory stimuli, exposure to compounds containing phenols,and/or exposure to weather conditions such as strong wind), overallhealth questions 818 (such as pregnancy, a presence of trauma, illness,depression, and/or hypoglycemia), premonitory questions 820, migrainequestions 822, medicine usage questions 824 (such as preventivetherapies, vitamins, herbs, oral contraceptives, estrogen replacementtherapy, recreational drugs, and/or pharmacological agents, includinganalgesics, other than migraine-specific drugs such as triptans), and/orderived variable(s) questions 826.

The premonitory questions 820 include symptoms that may be experiencedand/or exhibited by at least the first individual during the prodromephase of a migraine attack. Premonitory symptoms may include excitatorysymptoms, inhibitory symptoms and/or localized pain (for example, in thehead, neck and/or shoulders). Excitatory symptoms may include cravings(such as hunger and/or thirst), increased activity, sweating, a sense ofwell being, emotional changes (such as irritability), increasedurination or bowel movements, and/or increased sensitivity to sensorystimuli. Inhibitory symptoms may include confusion, difficultyconcentrating, depression, dizziness, fatigue, constricted circulation,yawning, and/or a lack of appetite.

The migraine questions 822 may include migraine occurrence information,migraine information (such as pain location, severity, quality ordescription, patterns, and/or temporal variation), use of medicines(including pharmacological agents such as one or more acute therapies),use of non-pharmacological treatments, a presence of visualdisturbances, symptoms of cutaneous allodynia, and/or other migrainessymptoms (such as nausea and/or vomiting). The migraine occurrenceinformation may include one or more temporal onsets or onset times.

In some embodiments, at least the first individual may be asked thederived variable(s) questions 826. One or more answers to the derivedvariable(s) questions 826 may be determined in accordance with one ormore answers to one or more pre-determined questions in one of the otherquestionnaire modules. For example, exposure to a food containingtyramine may be determined based on one or more answers to one or morepre-determined questions in the dietary questions module 812. One ormore answers to the derived variable(s) questions 826 may be determinedin accordance with a mapping operation performed on one or more answersto one or more pre-determined questions in the dietary questions module812. For example, one or more foods consumed (such as mayonnaise) may bemapped to basic constituents (egg, vinegar, and/or mustard) and/orelemental constituents (minerals, fats, carbohydrates, and proteins).

As discussed below with reference to FIG. 20, the mapping may also beperformed in the reverse direction (i.e., from one or more constituentsto compound foods that contain these constituents), with a correspondingimpact on the pattern(s) of occurrence. For example, a post-mappingpattern of occurrence for egg may include the pre-mapping pattern ofoccurrence for egg plus the post-mapping pattern of occurrence formayonnaise, plus patterns of occurrence for other foods that includeegg. For categorical data, entries in a respective post-mapping patternof occurrence may be determined by performing a logical operation, suchas an OR operation, on the corresponding entries in the patterns ofoccurrence that are combined to produce the respective post-mappingpattern of occurrence.

These mapping operations may be performed using tables of relatedinformation, such as one or more recipes and/or elemental constituentinformation. Elemental constituent information for some foods may beobtained in the National Nutrient Database on the United StatesDepartment of Agriculture's website atwww.nal.usda.gov/fnic/foodcomp/Data.

One or more answers to the derived variable(s) questions 826 may also bedetermined in accordance with additional public information, such asweather (conditions and/or changes), altitude, allergen, and/orpollution information. For example, pollution information may beobtained from the United States Environmental Protection Agency'swebsite at www.epa.gov/air/data. In some embodiments, one or moreanswers to the derived variable(s) questions 826 may be determined inaccordance with at least the first individual's location(s) during thedata-collection time interval, which may be provided or determined usingthe optional location module 370 (FIG. 3).

The pre-determined questions in one or more of the questionnaire modulesmay correspond to deviations from normal or usual behavior for at leastthe first individual. For example, deviations from normal sleep patternsfor at least the first individual, deviations from normal behavior whileat least the first individual is awake, and/or deviations from normaldietary behavior for at least the first individual.

In some embodiments, a respective questionnaire module, such as thedietary questions 812, may include primary questions, such as theprimary questions 610 (FIG. 6A), and secondary questions, such as thesecondary questions 620 (FIG. 6A). For example, the dietary questions812 may include are primary questions such as pre-determined questions828 (“Did you miss a meal today?”), 832 (“Did you have your snack at theusual time?”) and 836 (“Did you eat at a restaurant today?”).Pre-determined question 830-1 (Did you miss breakfast today?”),pre-determined question 830-2 (“Did you miss lunch today?”),pre-determined questions 830-3 (“Did you miss dinner today?”),pre-determined question 834-1 (“Was the snack early or late?”) andpre-determined question 834-2 (“How [Early or Late] was the snack?”) maybe secondary questions. Note that pre-determined (secondary) question834-2 may depend on the answer to pre-determined (secondary) question834-1.

At least some of the dietary questions 812 may be displayed using aformat such as that illustrated in FIG. 6A. Other questions in one ormore other questionnaire modules may be displayed using a format such asthat illustrated in FIG. 6B. For example, medicine usage questions 824and/or the use of medicines pre-determined questions in the migrainequestions 822 may be displayed using the format in FIG. 6B. Pre-selectedanswers 616 (FIG. 6A) for some of the pre-determined questions maycorrespond to a usual or a normal behavior for at least the firstindividual in accordance with at least the first individual's answerhistory 360 (FIG. 3), the answer history 360 (FIG. 3) for one or moregroups (such as men, women, an age group, a demographic group, groups ofmigraine patients, and/or groups of migraine patients having one or moremigraine triggers in common), one or more answers to the optionalinitial survey, and/or one or more default answers 362 (FIG. 3).

Answers, be they pre-selected or not, to the pre-determined questions inthe dietary questions 812, as well as in one or more other questionnairemodules, may be categorical, ordered categorical, and/or quantitative.For example, the answer to pre-determined question 828 may be ‘yes’ or‘no’. The answer to pre-determined (secondary) question 834-2 may bequantitative (a time in hours, minutes and/or seconds) and/or orderedcategorical (between 0-1 hours, between 1-2 hours, etc.). Orderedcategorical answers to some of the primary or secondary questions mayinclude time intervals that correspond to a fraction of a day. In anexemplary embodiment, a presence or absence of a respective variable maybe determined in accordance with a selection of a ‘yes’ answer to aprimary question and a selection of one or more time intervals thatcorrespond to a fraction of a day in answer to a related secondaryquestion. The time intervals may include night, morning, afternoon andevening, where night is between 12 am and 5.59 am, morning is between 6am and 11.59 am, afternoon is between 12 pm and 5.59 pm and evening isbetween 6 pm and 11.59 pm. In some embodiments, at least some questionsin one or more of the questionnaire modules may have different orderedcategorical time intervals associated with them than other questions inthe one or more of the questionnaire modules.

The questionnaire data structure 800 may include fewer or additionalquestionnaire modules. One or more of the questionnaire modules mayinclude one or more questions corresponding to feedback from at leastthe first individual and/or suggestions for additional variables to betracked (information to be collected) and/or analyzed. Such feedback mayallow a knowledge base to grow and improve as the approach is scaled tomore individuals. Two or more questionnaire modules may be combined.Some pre-determined questions may be included in more than onequestionnaire modules. One or more questionnaire modules may includefewer or more pre-determined questions. In some embodiments, one or morepre-determined questions may be moved from one questionnaire module toanother.

As noted previously, the subset of pre-determined questions may bevaried during the data-collection time interval, i.e., the questionnairemay be used dynamically. The varying may be in accordance with theconfiguration instructions 420 (FIG. 4), with providing of one or morepre-determined questions 354 in FIGS. 3 and 4 (for example, in a datastream that is transmitted and stored in the memory device 324 in FIG.3), with providing of instructions corresponding to one or morepre-determined questions (such as in instructions corresponding to oneor more web pages 412 in FIG. 4), and/or with providing of the optionalmemory device 524 in FIG. 5 containing one or more pre-determinedquestions 354.

In some embodiments, an initial phase of the data-collection timeinterval may, at least in part, correspond to a training phase, for atleast the first individual (i.e., how to answer the pre-determinedquestions), for one or more of the apparatuses, and/or for one or morealgorithms or techniques for implementing the questionnaire (forexample, how best to determine the one or more temporal onsets for atleast the first individual). During the training phase, the subset ofpre-determined may initially include one or more pre-determinedquestions selected from the premonitory questions 820, the migrainequestions 822 and/or the medicine usage questions 824.

The questionnaire, and the related statistical analysis (described belowwith reference to FIGS. 9-14), may be applied iteratively. For example,pre-determined questions in one or more of the questionnaire modules maybe tree-based or hierarchical, ranging from general or broad in scope tonarrow or specific in scope. General pre-determined questions may beasked one or more times during the data-collection time interval. Basedon one or more answers to these general pre-determined questions,additional narrow pre-determined questions may be asked one or moretimes. In some embodiments, the subset of pre-determined questions maybe asked, one or more association variables (for example, migrainetriggers) may be identified and at least the first individual mayexclude one or more of the identified association variables (forexample, by modifying behavior, changing diet, etc.). This process ofasking, identifying and excluding may be repeated one or more timesuntil diminishing returns (for example, it may become difficult toreadily and/or reliably identify one or more additional associationvariables).

FIG. 8B is a block diagram illustrating an embodiment of a questionnaire850 that is dynamic and hierarchical. One or more pre-determinedquestions in question modules are included in the subset ofpredetermined questions as a function of time 852. General sleep patternquestions 854 may be included. Specific sleep pattern questions 856 maybe included on two occasions. General behavioral questions and specificmigraine questions 858 may be included. Specific environmental questions860 may be included. The questionnaire 850 is meant to be illustrativeof a dynamic questionnaire and is not indicative of a specificimplementation. Thus, there may additional or fewer portions of questionmodules, additional or fewer question modules, an order or two or moreof the question modules may be changed, at least a portion of two ormore the question modules may be combined, and/or at least a portion ofone or more additional question modules may be included at any instancein time.

In order to reduce or eliminate inaccuracies associated with memory orrecall errors, in some embodiments at least the first individual may notbe allowed to answer or modify answers in one or more subsets ofpre-determined questions, such as those in the questionnaire 850, thatwere asked at previous instances in time that are greater than apre-determined value, for example, one or more days prior to the currentquestionnaire.

Attention is now directed towards embodiments of the statisticalanalysis, including the determination of one or more statisticalrelationships between one or more temporal onsets and one or morevariables and/or one or more compound variables, and the identificationof one or more association variables. The statistical analysis mayinclude classification and/or regression (such as determining a model ofthe temporal onsets including one or more variables and/or one or morecompound variables with corresponding weights). FIG. 9A is a blockdiagram illustrating an embodiment 900 of determining a compoundvariable 920 associated with events having different temporal onsets910. In some embodiments, the events may be migraine attacks and thetemporal onsets 910 may be onset times for migraine attacks. Temporalonsets 910 are shown as a function of time 908. The temporal onsets 910may include one or more onset times and/or one or more onsets during oneor more time windows or time intervals. There is a time delay 922between temporal onset 910-1 and temporal onset 910-2. An inverse of thetime delay 922 may correspond to a frequency of the events. Patterns ofoccurrence of variable A 914 and variable D 916, including instances orentries corresponding to presence information (illustrated by arrows)and corresponding to absence information (illustrated by absences ofarrows), as function of time 908 are illustrated on separate butidentical axes for clarity.

The compound variable 920 may correspond to at least a pattern ofoccurrence of variable A 914 during a first time interval 912 precedingthe temporal onsets 910 and a pattern of occurrence of variable D 916during a second time interval 918 preceding the temporal onsets 910.Note that in embodiment 900, the first time interval 912 may be offset924 from the temporal onsets 910. In some embodiments, the first timeinterval 912, the second time interval 918, and/or additional timeintervals corresponding to additional variables may have correspondingoffsets from the temporal onsets 910. In some embodiments, a pattern ofoccurrence of at least one variable may be in accordance with one ormore time intervals having a width that corresponds to a precision of atime measurement, i.e., the one or more time stamps that correspond torespective times.

In some embodiments, the first time interval 912, the second timeinterval 918, and/or other time intervals may have the same durationand/or offsets 924. In some embodiments, the first time interval 912,the second time interval 918, and/or other time intervals may have adifferent duration and/or offsets 924. In some embodiments, one or moreof the time intervals may be adjustable. In exemplary embodiments, thetime intervals may have a duration of a fraction of a day (such as 1, 2,3, 4, 6, 12, and/or 18 hours), one day, two days, three days, more days,and/or combinations of these items. In some embodiments, offsets, suchas offset 924, may be between 0 and up to 3, 5, and/or 10 or more days.In exemplary embodiments, the offset 924 may be a fraction of a day(such as 1, 2, 3, 4, 6, 12, and/or 18 hours), one day, two days, threedays, more days, and/or combinations of these items.

A respective instance or entry for the compound variable 920, such ascompound variable 920-1, may correspond to a presence if variable A ispresent during the first time interval 912-1 (a presence entry in thepattern of occurrence of variable A 914) and/or if variable D is presentduring the second time interval 918-1 (a presence entry in the patternof occurrence of variable D 916). Alternatively, a respective instanceor entry for the compound variable 920, such as compound variable 920-2,may correspond to an absence if variable A is absent during a first timeinterval 912-2 (an absence entry in the pattern of occurrence ofvariable A 914) and/or if variable D is absent during a second timeinterval 918-2 (an absence entry in the pattern of occurrence ofvariable D 916).

In an exemplary embodiment, entries for the pattern of occurrence ofvariable A 914 during the first time interval 912 and the pattern ofoccurrence of variable D 916 during the second time interval 918 may becategorical or may be converted from quantitative to categorical bypartitioning using one or more thresholds. In some embodiments,different thresholds may be used for different variables. In someembodiments, one or more compound variables may be a weighted summationof one or more variables. The resulting one or more compound variablesmay be converted into categorical data using one or more thresholdsand/or one or more quantitative variables may be converted intocategorical data using one or more thresholds prior to generating one ormore compound variables using a weighted summation.

Note that entries in the patterns of occurrence for categoricalvariables are typically represented by codes. For categorical variableshaving two classes or categories, a single binary digit may be used,such as 0 or 1, or −1 or 1. When there are more than two categories,such as with ordered categorical variables, a dummy variable having Kvalues or bits may be used. In addition, in some embodiments anadditional digit or symbol is used to indicate missing data (such aswhen at least the first individual fails to complete a questionnaire onone or more occasions). Entries for the compound variable 920 maydetermined by performing an operation and/or a logical operation oncorresponding entries in the pattern of occurrence of the variable A 914and the pattern of occurrence of the variable D 916. The operation mayinclude multiplication. The logical operation may include a Booleanoperation, such as AND. A wide variety of coding approaches, however,may be used in different embodiments for representing presence andabsence information in the pattern of occurrence of variable A 914 andthe pattern of occurrence of variable D 916. Therefore, in someembodiments the logical operation may include AND, OR, NOT, XOR, as wellas combinations of these operations. Note that in those embodimentswhere the compound variable 920 includes cross-terms that correspond tothe pattern of occurrence of the variable A 914 and the pattern ofoccurrence of the variable D 916 the resulting analysis includesnonlinear terms.

While FIG. 9A illustrates two variables, in some embodiments one, threeor more variables may be used to determine the pattern of occurrence(including presence and absence information) for the compound variable920. While a respective variable has a corresponding time interval andoffset (which may be zero or finite), in some embodiments at least twovariables may have time intervals having the same duration and/or thesame offset. Similarly, while FIG. 9A illustrates two temporal onsets910, in some embodiments there may be one temporal onset 910 or three ormore temporal onsets 910, which may be used in determining the patternof occurrence of the compound variable 920.

FIG. 9B is a block diagram illustrating an embodiment 950 thatsummarizes the determining of compound variables. Logical operations areperformed on the patterns of occurrence of one or more subsets ofvariables, such as variable A (during time interval I) 960 and variableB (during time interval II) 962, and variable D (during time interval I)964, variable A (during time interval II) 966 and variable B (duringtime interval II) 962.

A number of variables included in determining a respective compoundvariable is henceforth referred to as an order n. FIG. 9B illustratesdetermination of a compound variable of order 2 and a compound variableof order 3. In some embodiments, a respective variable during a timeinterval, such as variable D (during time interval I) 964, may beincluded once in determining a respective compound variable, i.e.,multiple instances of the respective variable during the time intervalmay not be included in determining the respective compound variable.However, the respective variable may be included more than once indetermining the respective compound variable if different time intervalsare used, such as variable A (during time interval I) 960 and variable A(during time interval II) 966. In some embodiments, there may beadditional or fewer variables, i.e., the order may be 1 (a respectivecompound variable is merely a variable) or 4 or more. In someembodiments, time interval I may correspond to a duration of 24 hourswith an offset 924 (FIG. 9A) of 0, 24 or 48, 72 and/or 96 hours from thetemporal onsets 910 (FIG. 9A). Time interval II may correspond to aduration of 24 hours with an offset 924 (FIG. 9A) of 0, 24 or 48, 72and/or 96 hours from the temporal offsets 910 (FIG. 9A). In someembodiments, there may be additional or fewer variables included in arespective compound variable, there may be fewer or additional timeintervals, and/or there may be fewer or additional offsets.

Referring back to FIG. 9A, as discussed further below one or morestatistical relationships between the patterns of occurrence of one ormore compound variable, such as compound variable 920, and/or thepattern of occurrence of one or more of the variables, such as variableA 914, and the temporal onsets 910 may be determined. In the case ofmigraines, however, one or more temporal onsets 910 corresponding to oneor more rebound headaches, one or more recurrence headaches, and/or oneor more tension headaches may be excluded during the determining of theone or more statistical relationships. In some embodiments, one or moretemporal onsets 910 may be excluded if there is missing data in one ofthe time intervals 912 and/or 918. For example, the first individual maynot have logged data on one or more days during the data-collection timeinterval.

Excluding some of the temporal onsets 910 may improve the results of thestatistical analysis. For example, the one or more rebound headaches maybe identified in accordance with a medicine usage history forpharmacological agents, such as analgesics and/or triptans. In someembodiments, the one or more rebound headaches may be identified, atleast in part, if there is no pain-free period between migrainesattacks. In some embodiments, a subset of the temporal onsets 910 may beused in the calculations to increase a magnitude of the determinedstatistical relationship.

In addition, entries in the pattern of occurrence of one or morevariables that occur during the duration of an event, such as amigraine, may be excluded in determining one or more compound variablesand/or one or more statistical relationships. The reason for thisexclusion operation may be that such entries, corresponding to thepresence of one or more variables, may not trigger an event since anevent is already occurring. Said differently, it may not be possible toinitiate something that is already occurring. FIG. 10 is a block diagramillustrating an embodiment 1000 with a variable D 916-2 occurring duringa duration 1010 of a migraine. As a consequence, the presence ofvariable D 916-2 may be excluded from the determining of compoundvariable 920 (FIG. 9A) and/or one or more statistical relationships,such as those between temporal onsets 910 and the pattern of occurrenceof the compound variable 920 (FIG. 9A) and/or between temporal onsets910 and the pattern of occurrence of variable D 916. Note that thetemporal onset 910-1 is illustrated as occurring during a time interval1014. In some embodiments, the temporal onset 910-1 corresponds to anonset time (i.e., a specific time). In alternate embodiments, theduration 1010 may be defined with respect to a beginning of the timeinterval 1014, a center of the time interval 1014, or the onset timecorresponding to the temporal onset 910-1. Embodiment 1000 alsoillustrates a threshold 1012 that may be used to convert a quantitativevariable into a categorical variable by partitioning. In otherembodiments, one or more thresholds may include one or more geographicdirections.

While embodiments 900 (FIG. 9A) and 1000 illustrate variables, such asthe variable D 916, occurring in time intervals 912 (FIG. 9A) and 918(FIG. 9A) preceding corresponding temporal onsets 910, in someembodiments occurrences of one or more variables and one or moretemporal onsets 910 in one or more time intervals may be included whendetermining one or more of the statistical relationships. While theembodiment 1000 illustrates the exclusion of the presence of variable D916-2, in some embodiments entries corresponding to an absence of one ormore variables may be excluded from the determination of one or morestatistical relationships. This may be separate from and/or in additionto the exclusion of the presence of the one or more variables.

A wide variety of computational techniques may be used to determine theone or more statistical relationships, including one or more parametricanalysis techniques, one or more non-parametric analysis techniques, oneor more supervised learning techniques and/or one or more unsupervisedlearning techniques. In some embodiments, one or more non-parametricanalysis techniques may be used. As noted previously, non-parametricanalysis techniques make few assumptions about an existence of aprobability distribution function, such as a normal distribution,corresponding to a population from which samples or entries areobtained, or regarding independence of the variables and/or the compoundvariables. In general, non-parametric analysis techniques may use rankor naturally occurring frequency information in the data to drawconclusions about the differences between populations.

The one or more non-parametric analysis techniques may performhypothesis testing, i.e., to test a statistical significance of ahypothesis. In particular, the one or more non-parametric analysistechniques may determine if the one or more temporal onsets and the oneor more compound variables and/or one or more variables arestatistically independent (or dependent) in accordance with astatistical significance criterion. One or more variables and/or one ormore compound variables having a statistically significant relationshipwith the temporal onsets may be used to identify one or more associationvariables. In the case of migraines, the one or more associationvariables may be migraine triggers or potential migraine triggers.

In exemplary embodiments, the non-parametric analysis technique mayinclude a chi-square analysis technique, a log-likelihood ratio analysistechnique (also referred to as G-test), and/or a Fisher's exactprobability analysis technique. In addition to their other advantages,these techniques may be well suited to analyzing an underdeterminedproblem (i.e., sparse sampling in a multi-dimensional variable space),in which there may be a plurality of variables and/or compound variablesand a limited number of entries or samples.

The chi-square analysis technique, the log-likelihood ratio analysistechnique, and the Fisher's exact probability analysis technique may bedetermined using a cross-tabulation or contingency tables (sometimesreferred to as bivariate tables). The Fisher's exact probabilityanalysis technique computes the sum of conditional probabilities ofobtaining the observed frequencies in a respective contingency table andthe conditional probabilities of obtaining exactly the same observedfrequencies for any configuration that is more extreme (i.e., having asmaller conditional probability). The chi-square (χ²) may be determinedusing

${\chi^{2} = {\sum\limits_{i}\frac{\left( {O_{i} - E_{i}} \right)^{2}}{E_{i}}}},$and the log-likelihood ratio (LLR) using

${{LLR} = {\sum\limits_{i}{O_{i}{\ln\left( \frac{O_{i}}{E_{i}} \right)}}}},$where the summation is over the entries in the respective contingencytable, O_(i) is the i-th observed frequency value, and E_(i) is the i-thexpected frequency value. The LLR tests the likelihood of one hypothesis(the alternate hypothesis) against another, more restrictive hypothesis(the null hypothesis). In an exemplary embodiment, the alternatehypothesis is that the conditional probabilities are different from 0.5and the null hypothesis is that the conditional probabilities are 0.5.The following example illustrates an embodiment of determining astatistical relationship using the log-likelihood ratio.

Consider the data in Table IV. The first column contains the number ofentries in the pattern of occurrence where a variable or compoundvariable is present during a time interval, such as the first timeinterval 912 (FIG. 9A), and a temporal onset is present after a timeoffset, such as the time offset 924 (FIG. 9A) (henceforth denoted byX₁₁) plus the number of entries in the pattern or occurrence where thevariable or compound variable is absent during the time interval and atemporal onset is absent after the time offset (henceforth denoted byX₀₀). X₁₁ is sometimes referred to as a true-true and X₀₀ is sometimesreferred to as a false-false. X₁₁ and X₀₀ are henceforth referred to asco-occurrences.

The second column contains the number of entries in the pattern ofoccurrence where the variable or compound variable is present during thetime interval and a temporal onset is absent after the time offset(henceforth denoted by X₁₀) plus the number of entries in the pattern ofoccurrence where the variable or compound variable is absent during thetime interval and a temporal onset is present after the time offset(henceforth denoted by X₀₁). X₁₀ is sometimes referred to as atrue-false and X₀₁ is sometimes referred to as a false-true. X₁₀ and X₀₁are henceforth referred to as cross occurrences.

TABLE IV An embodiment of a contingency table. Number of Number ofCo-Occurrences (X₁₁ + X₀₀) Cross Occurrences (X₁₀ + X₀₁) 46 11

If the variable or the compound variable and the temporal onsets arecompletely independent, the expected frequency values for each column,E₁ and E₂, would equal 28.5, one half of the conditional probabilitytimes the sum of the number of co-occurrences and cross-occurrences,i.e., the total number of observations (data points or samples) in TableIV. Therefore, for Table IV

${LLR} = {{{{2 \cdot 46}\mspace{11mu}{\ln\left( \frac{46}{28.5} \right)}} + {{2 \cdot 11}\;{\ln\left( \frac{11}{28.5} \right)}}} = {{44.04 - 20.94} = {23.10.}}}$A one-sided minimal statistical significance confidence criterion of 5%(α=0.05) or statistical confidence threshold, based on the 1 degree offreedom in this example, corresponds to an LLR of 3.841. Since the LLRfor Table IV is greater than 3.841, the LLR, and thus the alternatehypothesis, is statistically significant. From a statisticalsignificance perspective, therefore, the temporal onsets and the patternof occurrence of the variable or compound variable in this example aredependent. Note that the determination of the statistical relationshipfor the temporal onsets and the variable or the compound variable inthis embodiment uses presence and absence information in the pattern ofoccurrence of the variable or compound variable. In some embodiments,one or more of the statistical relationships may be determined usingpresence information, i.e., the presence of one or more variables or oneor more compound variables during one or more time intervals, withoutusing absence information. In some embodiments, a statisticallysignificant LLR also has a number of co-occurrences greater than anumber of cross-occurrences (i.e., it is related to migraines). And insome embodiments, a statistically significant LLR also has a number ofcross-occurrences greater than a number of co-occurrences (i.e., it isanti-related to migraines). In this way, migraine variables and/oranti-migraine variables may be identified. In alternate embodiments, awide variety of analysis techniques may be used to determine the one ormore statistical relationships, including one or more non-parametricanalysis techniques and one or more parametric analysis techniques.

In parametric analysis, a Pearson's product-moment correlationcoefficient r may be useful in summarizing a statistical relationship.For some contingency tables, Cramer's phi φ, the square root of χ² orthe LLR divided by the number of observations N, may have a similarinterpretation to r (although, it is known that Cramer's phi φ mayunderestimate r). In the example illustrated in Table IV,

$\varphi = {\sqrt{\frac{LLR}{N}} = {\sqrt{\frac{23.1}{57}} = {0.64.}}}$

The chi-square analysis technique and the log-likelihood ratio analysistechnique may have a maximal sensitivity for contingency tables based onpatterns of occurrence of variables or compound variables having 50%presence entries and 50% absence entries. In addition, in embodimentswhere temporal onsets, such as temporal onsets 910 (FIG. 9A), correspondto onsets during one or more time windows or time intervals, maximalsensitivity may occur if 50% of these time windows or time intervalshave a temporal onset (i.e., a presence entry). In some embodiments, oneor more contingency tables may be generated to achieve approximately 50%presence entries for patterns of occurrence of one or more variables orone or more compound variables, and/or 50% temporal onsets by using asubset of the collected information or data. In an exemplary embodiment,one or more contingency tables may be generated by approximatelyrandomly (including the use of a pseudo-random number generator oralgorithm) selecting a subset of the temporal onsets, and/orapproximately randomly selecting a subset of the presence or absenceentries of one or more patterns of occurrence of one or more variablesor one or more compound variables such that the one or more contingencytables may have approximately 50% presence entries and 50% absenceentries distributed over X₀₀, X₁₁, X₁₀, and X₀₁. For infrequentlyoccurring events, variables, and/or compound variables, there may bemore absence entries than presence entries in the collected data orinformation. As a consequence, different sampling ratios may be used forpresence and absence entries.

In some embodiments, boosting may be used when generating one or morecontingency tables. The fraction of the collected information may beapproximately randomly sampled to generate one or more contingencytables. A respective contingency table may be generated K times usingapproximate random sampling. Statistical relationships for at least M ofthese K contingency tables may be used (including combining and/oraveraging) to determine whether or not the temporal onsets and thecorresponding variable or compound variable are independent ordependent. In an exemplary embodiment, K may be 5, 10, 25, 50, 100 ormore. M may be 50% (rounded to the nearest integer), 60%, 66%, 70%, 75%,80% or more of K.

In some embodiments, there may be too few presence entries or too manypresence entries in one or more patterns of occurrence of one or morevariables or compound variables to reliably determine statisticallysignificant independence (or dependence) from the temporal onsets. As aconsequence one or more of these variables or one or more of thesecompound variables may be excluded when determining one or morestatistical relationships. In an exemplary embodiment, one or morevariables or one or more compound variables having patterns ofoccurrence with less than 10% presence entries or more than 85% presenceentries may be excluded. In another exemplary embodiment, one or morevariables or one or more compound variables having patterns ofoccurrence with less than 4 presence entries or less than 10 absenceentries may be excluded. To assist in obtaining sufficient presence andabsence entries, in some embodiments at least the first individual maybe instructed to vary their activities and/or diet from day to dayduring the data collection time interval.

Overfitting is a risk when developing a model in a statistical learningproblem. In some embodiments, this risk may be addressed by using afraction or percentage of the collected data or information (patterns ofoccurrence and temporal onsets) for training, i.e., to develop themodel, and a remainder for testing the resulting model. This isillustrated in FIG. 11, which is a block diagram illustrating anembodiment 1100 of determining model complexity. In some embodiments,the model complexity may correspond to a number of variables or compoundvariables that have statistically significant dependence on the temporalonsets. In some embodiments, the model complexity may, at least in part,correspond to a number of variables included in a respective compoundvariable, i.e. the order n. Embodiment 1100 shows a magnitude of atraining and/or a test error 1112 as a function of model complexity1110. A training error 1114 typically decreases as the model complexity1110 increases (the model better fits or predicts a training set ofdata). A test error 1116 typically exhibits a minimum. Additional modelcomplexity 1110 beyond this point does not generalize well (the modeloffers a poorer fit or prediction for a test set of data). Beyond thispoint, therefore, the training set of data may be overfit 1118. Inexemplary embodiments, the percentage of the collected information usedfor training may be 70%, 75%, 80%, 85% or 90%.

An additional metric of the model complexity may be determined. Thismetric may be used in conjunction with or independently of the trainingset of data and the test set of data. The additional metric is describedbelow. In some problems and/or embodiments, determining one or morestatistical relationships for one or more variables (or, saiddifferently, one or more compound variables of order 1) may not besufficient to determine statistically significant independence (ordependence) with respect to the temporal onsets. For example, inmulti-dimensional problems, where exposure to two or more variables inat least close temporal proximity may be necessary to initiate atemporal onset (such as a migraine), a value of the Fisher's exactprobability, χ², LLR for a compound variable of order 1, and/or anothermetric of statistical relationship may be reduced since there is apenalty for the presence of the cross occurrences, X₁₀ and X₀₁.

More generally, the value of the Fisher's exact probability, χ², LLR,and/or another metric of statistical relationship may be reduced if theorder n of one or more compound variables is less than an intrinsicorder of the multi-dimensional problem. In the case of X₁₀, a temporalonset may or may not occur unless a certain number of variables or a setof variables (which may be inter-operative) are present in closetemporal proximity. And in the case of X₀₁, more than one set ofvariables may be present, i.e., one or more variables in another set ofvariables may have triggered the corresponding temporal onsets. Asillustrated in FIG. 7, in the embodiments for migraines there may alsobe variations in a patient's sensitivity threshold to a variable or oneor more sets of variables as a function of time.

To assess whether or not the model has sufficient complexity, i.e.,whether or not one or more compound variables have been determined tosufficient order n, a ratio R may be determined. R is defined as X₁₁divided by the total number of occurrences of the variable or compoundvariable of order n, i.e.,

$R = {\frac{X_{11}}{\left( {X_{11} + X_{10}} \right)}.}$An increasing value of R, and/or Cramer's phi φ, as statistical analysisis performed to higher order (i.e., n+1) may be metrics of goodness,i.e., it may indicate that the higher order does a better jobdetermining statistically significant independence or dependence betweenone or more compound variables and the temporal onsets. In someembodiments, contingency tables for one or more compound variables maybe generated for progressively higher orders. Once the ratio R is closeto or equal to one, i.e., X₁₀ is close to or equal to zero, furtherincreases in the order of one or more compound variables may not beneeded, i.e., the model has sufficient complexity.

One or more variables and/or compound variables having statisticallysignificant statistical relationships with the temporal onsets may beidentified as one or more association variables. For a respectivecompound variable or order n having a significant statisticalrelationships with the temporal onsets, the n constituent variables maybe identified as n association variables and/or as a set of associationvariables. In some embodiments, one or more statistically significantcompound variables of order n having the ratio R approximately equal to1 may be identified as one or more association variables. In theembodiments for migraines, one or more association variables may be oneor more migraine triggers or one or more probable migraine triggers forat least the first individual.

In some embodiments, one or more compound variables of order n and/orone or more constituent variables in the one or more compound variablesof order n may be ranked in accordance with the corresponding determinedstatistical relationships that are statistically significant. In someembodiments, a ranking of a respective statistically significantconstituent variable and/or a respective compound variable is inaccordance with a number of associated migraine onsets. In someembodiments, a ranking of a respective constituent variable is inaccordance with a number of occurrences of the respective constituentvariable in one or more compound variables of order n having statisticalrelationships that are statistically significant. Furthermore, in someembodiments one or more of the ranking techniques described above mayinclude multiplicative weights. For example, a ranking may be inaccordance with a product of the number of occurrences of the respectiveconstituent variable times an average number of associated migraineonsets that correspond to these occurrences.

Ranking may be performed as the statistical significance confidencecriterion (α) is progressively increased. A respective rankingcorresponding to a respective statistical significance criterion α maybe meaningful if a total number of variables and/or compound variables Sused in the analysis times a number of variables and/or compoundvariables having statistical relationships greater than α is greaterthan α times S.

In exemplary embodiments, α may be 0.05 or lower. For a respectiveranking, a pareto corresponding to at least a subset of the respectiveranking may be defined. The pareto may correspond to variables orcompound variables having a statistical relationship or a number ofoccurrences exceeding a threshold. In some embodiments, a top 10, 20, 50or 100 variables or compound variables may be used, or a plurality ofthe top 10, 20, 50 or 100 variables or compound variables may be used.For compound variables of order n, approximate stability of the paretoas the statistical significance confidence criterion is increased may beused to identify a noise floor. Approximately stability may include anapproximately unchanged order n in the ranking or a presence ofapproximately the same variables (for example, more than 70% of them) inthe ranking. In exemplary embodiments, the noise floor may correspond toan α of 0.01 or lower, an α of 0.001 or lower, or an α of 0.0001 orlower. In some embodiments, for a respective statistical significancecriterion α a noise floor in a ranking may correspond to an uncertaintyin the statistical relationships for one or more variables and/or one ormore compound variables that is associated with missing data during thedata-collection time interval (such as data that was not logged for oneor more days). One or more variables and/or one or more compoundvariables in paretos corresponding to one or more statisticalsignificance confidence criteria that exceed a noise floor may beidentified as association variables.

In some embodiments, a background ranking that corresponds to noise inthe data may be determined. In an exemplary embodiment, the backgroundranking may be determined using temporal onsets that are randomly orpseudo-randomly selected. In another exemplary embodiment, thebackground ranking may correspond to one or more variables and/or one ormore compound variables that do not have a statistic relationship withthe temporal onsets (for example, an LLR of infinity). The backgroundranking may be subtracted from one of the previously described rankingsto correct the results for a background contribution. This may be usefulsince in some embodiments the analysis technique described above withreference to FIGS. 9-10 is a form of nonlinear analysis (which issometimes referred to as nonlinear feature extraction). As such,contributions corresponding to intermodulation products may occur in thestatistical relationships between the temporal onsets and the one ormore variables and/or the one or more compound variables. Furthermore, aratio of a number of absence entries to a number of presence entries inthe pattern of occurrence that is used in a respective contingency tablemay also increase the background contribution. For migraines, as thisratio is increased, the previously described rankings may increasinglycorrespond to a background contribution. Subtracting the backgroundcontribution may, therefore, reduce and/or eliminate such effects fromthe results.

In some embodiments, one or more variables and/or one or more compoundvariables in paretos corresponding to one or more statisticalsignificance confidence criteria that exceed the noise floor may be usedas a seed set in a subsequent statistical analysis. The subsequentstatistical analysis may determine statistical relationships forcompound variables of a higher order. In some embodiments, thesubsequent analysis may utilize an analysis technique such as SVM orCART. These and other analysis techniques are discussed further below.

FIG. 12 is a block diagram illustrating an embodiment of rankingvariables 1200. Statistical relationship value 1212 is plotted as afunction of statistical significance 1210, such as the statisticalsignificance confidence criteria. Several rankings 1222 are illustrated.Ranking 1222-1, including variable F (during time interval IV) 1214 andvariable M (during time interval II) 1216, is below a noise floor 1218.Ranking 1222-2 and ranking 1222-3 are above the noise floor 1218. Asubset 1220 of ranking 1222-2 and ranking 1222-3 is stable. The subset1220 may identified as the pareto.

In an exemplary embodiment for migraines, the noise floor 1218corresponds to an α of approximately 0.001. At least 8 of the top-10variables in paretos for more stringent statistical significanceconfidence criteria than that corresponding to the noise floor 1218 arepresent even when an approximately random subset corresponding to 80% ofthe patterns of occurrence and the temporal onset data is used.Excluding probable recurrence headaches, rebound headaches and tensionheadaches increases the statistical relationship values 1212 forcompound variables having an order n corresponding to the pareto.Compound variables of at least order 4 have ratio R values approximatelyequal to 1.

Having identified one or more association variables for at least thefirst individual, one or more additional association variables may beidentified. For example, if one or more groups of association variableshave been previously determined for one or more other individuals, theone or more association variables identified for at least the firstindividual may be used to associate at least the first individual withone or more of these groups. In this way, one or more of the associationvariables in one or more of the groups may be identified as additionalassociation variables for at least the first individual. For example,the one or more additional association variables may be groups ofmigraine triggers and at least the first individual may be associated(classified) with one or more of these groups in accordance with one ormore identified migraine triggers for at least the first individual.

Alternatively, additional association variables may be identified byassociating the identified one or more association variables for atleast the first individual with previously determined groups ofvariables. For example, the identified one or more association variablesfor at least the first individual may be foods and additionalassociation variables may be identified by associating the foods withcorresponding food groups, such as pine-apple, mushroom, melon, cashew,banana, or citrus, or groups determined based on an amount ofconstituent elements (minerals, fats, carbohydrates, and/or proteins) infoods. For example, if an identified association variable is in the beetfamily or the citrus family, other members of the beet or citrusfamilies may be identified as association variables.

FIG. 13 is a block diagram illustrating an embodiment 1300 ofassociating one or more variables with one or more groups of variables.Group I 1310 may include variable A 1314, variable F 1316, associatedvariable S 1318, and variable C 1320. Group II 1312 may include variableC 1320 and a compound variable 1322, including variable A (during timeinterval III) plus variable D (during time interval I). If a variable,such as variable A 1314, is determined or identified, one or more of theother variables in group I 1310 may be identified. In some embodiments,there may be additional groups, there may or may not be overlap (such asvariable C 1320) between two or more of the groups, and/or a respectivegroup may include fewer or more variables, fewer or more associatedvariables, and/or fewer or more compound variables.

Having identified one or more association variables in accordance withone or more statistical relationships, rankings, and/or associatedgroups of associated variables, one or more recommendations and/or oneor more reports may be provided to at least the second individual and/orat least the first individual. In an exemplary embodiment, the one ormore recommendations may include a listing of one or more migrainetriggers and/or one or more probable migraine triggers for at least thefirst individual. The one or more recommendations may include one ormore variables for which the statistical analysis was unable todetermine a statistically significant relationship. In some embodiments,the one or more recommendations may indicate one or more migrainetriggers and/or one or more probable migraine triggers that at least thefirst individual may wish to modify (such as for behaviors) and/oravoid. The one or more recommendations may indicate additional analysisthat may be advisable in accordance with one or more of the statisticalrelationships and/or the one or more association variables. One or morecorresponding reports may include the one or more recommendations. Theone or more reports may include a summary for at least the firstindividual. The summary may include a health overview for at least thefirst individual during at least a portion of the data-collection timeinterval. In the case of migraines, the health overview (also referredto as a migraine diary) may include a summary of migraine frequency,migraine duration, migraine severity and/or the use of one or morepharmacological agents, such as one or more acute therapies and/or oneor more preventive therapies.

In some embodiments, the one or more recommendations may include placeboinformation, for example, placebo migraine triggers. After this placeboinformation is provided to at least the first individual (possibly viaan intermediary such as at least the second individual), an impact on atleast the first individual may be determined. For example, migrainefrequency, migraine duration, migraine severity, and/or use ofpharmacological agents during a subsequent time interval may bedetermined. An efficacy of the identified association variables may bedetermined by comparing these metrics with those that occur whennon-placebo information is used, i.e., when actual association variablesare provided to at least the first individual. The difference of thesetwo metrics can be used to define a therapeutic gain. In someembodiments, the therapeutic gain may be determined by averaging resultsfor two or more individuals such as at least the first individual.

Attention is now given to other techniques of performing statisticalanalysis, such as determining the one or more statistical relationships.As discussed previously, one or more variables or one or more compoundvariables determined during the statistical analysis, for example, inone or more paretos, may be used in subsequent analysis. In someembodiments, the subsequent analysis may utilize a non-parametricanalysis technique as an initial or first stage. In other embodiments,the subsequent analysis may not utilize a non-parametric analysistechnique. The subsequent analysis may be used as the initial or firststage, to refine the model (including adding or removing one or morevariables and/or one or more compound variables), and/or identify one ormore association variables. The subsequent analysis may includeclassification and/or regression (such as determining a model of thetemporal onsets including one or more variables and/or one or morecompound variables with corresponding weights). As with the initialstatistical analysis, a wide variety of techniques may be used in thesubsequent analysis. Two such techniques, SVM and CART, are describedfurther below.

Embodiments of SVM are instances of supervised learning techniques thatmay be applied to classification and regression problems. For binaryclassification, a set of binary labeled data points (training data orexamples) is provided. SVMs may be used to determine an optimalseparation boundary, defined by the variables and/or compound variables,between two classes of data points. A separation boundary is optimal ifusing it as a decision rule to classify future data points minimizes anexpected classification error. For linearly separable data sets (i.e., aclass of absences, which may be indicated by −1, and a class ofpresences, which may be indicated by +1, may be separated by a line in 2dimensions, or a so-called hyperplane in higher dimensions), SVMs may beused to determine a maximal margin hyperplane. For the maximal marginhyperplane, a linear decision boundary may be positioned such that itseparates both classes and such that the distance to the closest pointfrom each class is maximized. For non-linearly separable data sets, sometraining data points may be allowed on the opposite or “wrong” side ofthe hyperplane, i.e., a classification error on the training data setmay be allowed and may be minimized, while the margin, measured betweenpoints on the “correct” side of the hyperplane, is maximized.

If a linear decision boundary is not sufficiently complicated to modelthe separation between classes accurately, the corresponding linearmodel may be transformed into a non-linear model by non-linearlytransforming the variables and/or compound variables into a possiblyhigher dimensional Euclidean space. A linear decision boundaryconstructed in such a higher dimensional Euclidean space may correspondto a non-linear decision boundary in the original space of variablesand/or compound variables. This approach is referred to as kernel SVM.

Depending on how the margin and training error are measured, and how atrade-off between maximizing the margin and minimizing the trainingerror is established, different types of SVMs may be obtained. In someembodiments, SVM may include standard 1-norm SVM (measuring the marginusing Euclidean distance, i.e., a L₂-norm, and the training error usinga L₁-norm), standard 2-norm SVM (measuring the margin using Euclideandistance, i.e., the L₂-norm, and the training error using the L₁-norm),and/or LP-SVM (measuring the margin using the L₁-norm and the trainingerror using the L₁-norm). Each of these 3 types of SVM may be a C-typeor η-type SVM. These two varieties correspond to different ways oftrading-off maximizing the margin against minimizing the training error.The 1-norm SVM, standard 2-norm SVM, and/or LP-SVM may be a C+/C− orη+/η− type (when errors on positive (+1) labeled training data areweighted differently than errors on negative (−1) labeled trainingdata).

The principle for binary classification described above may be extendedto regression, for example, by copying the regression data twice,shifting both copies in opposite directions (over a distance epsilon)with respect to the continuous output dimension or variable andestablishing a regression surface as a decision boundary between the twoshifted copies that may be regarded as two classes for binaryclassification. As a consequence, in some embodiments, regressionversions of SVMs corresponding to previously described SVMs may be used.

The decision boundary determined using one or more SVMs may be used todiscriminate between temporal onsets and non-temporal onsets. For binaryclassification, measures of goodness for the resulting model include aprediction accuracy that is better than predicting 50% of the positivedata (i.e., occurrences, which may be indicated by a +1) as positive(i.e., true positive predictions) and better than predicting 50% of thenegative data (i.e., absences, which may be indicated by a −1) asnegative (i.e., true negative predictions). Doing better than 50/50corresponds to doing better than random. In an exemplary embodiment, theresulting model successfully predicts at least 80-85% of the true-false(X₁₀) and false-false events (X₀₀), i.e., the true negatives, whilepredicting significantly more than 50% of the true positives correctly,i.e., false-true (false-true (X₀₁) and true-true events (X₁₁)).

CART is a non-parametric multivariate analysis technique. It involvesthe determination of a binary decision tree using the training set ofdata. Predictions based on the resulting tree may be compared to thetest set of data (cross validation). A decision tree provides ahierarchical representation of the feature space in which explanatoryvariables are allocated to classes (such as temporal onsets ornon-temporal onsets) according to the result obtained by followingdecisions made at a sequence of nodes at which branches of the treediverge. Branches or divisions of the tree may be chosen to provide thegreatest reduction in the entropy of the variables (for a classificationtree based on categorical data), such as a small or zero standarddeviation, or the greatest reduction in the deviation between thevariables (and/or compound variables) and one or more variables beingfit (for a regression tree based on quantitative data). A tree stopsgrowing when no significant additional reduction can be obtained bydivision. A node that is not further sub-divided is a terminal node. Itis associated with a class. A desirable decision tree is one having arelatively small number of branches, a relatively small number ofintermediate nodes from which these branches diverge, terminal nodeswith a non-zero number of entries, and high prediction power (correctclassifications at the terminal nodes). In some embodiments, CART may beused in conjunction with a gradient boosting algorithm, where eachboosted tree is combined with its mates using a weighted voting scheme.Gradient boosting may be used to force the binary decision tree toclassify data that was previously misclassified.

As noted above, a wide variety of statistical analysis techniques may beused to determine the one or more statistical relationships. These mayinclude one or more supervised learning techniques, one or moreunsupervised learning techniques, one or more parametric analysistechniques (such as a Pearson's product-moment correlation coefficient ror an inner product), and/or one or more non-parametric analysistechniques. Non-parametric analysis techniques may include a Wilcoxonmatched pairs signed-rank test (for ordinal or ranked data), aKolmogorov-Smirnov one-sample test (for ordinal or ranked data), adependent t-test (for interval or ratio data), a Pearson chi-square, achi-square test with a continuity correction (such as Yate'schi-square), a Mantel Heanszel chi-square test, a linear-by-linearassociation test, a maximum likelihood test, a risk ratio, an oddsratio, a log odds ratio, a Yule Q, a Yule Y, a phi-square, a Kappameasure of agreement, a McNemar change test, a Mann Whitney U-test, aSpearman's rank order correlation coefficient, a Kendall's rankcorrelation, a Krushcal-Wallis One-Way Analysis of Variance, and aTurkey's quick test.

Supervised learning techniques may include least-squares regression(including correlation), ridge regression, partial least-squares (alsoreferred to as partial correlation), a perceptron algorithm, a Winnowalgorithm, linear discriminant analysis (LDA), Fisher discriminantanalysis (FDA), logistic regression (LR), a Parzen windows classifier, a(k−) nearest-neighbor classification, multivariate adaptive regressionsplines (MARS), multiple additive regression trees (MART), SVM, LASSO (aregularized linear regression technique like ridge regression, but withL₁-norm regularization of the coefficients), least angle regression(LARS), decision trees (such as CART, with and without gradientboosting, such as ID3 and C4.5), bagging, boosting (such as, adaboost)of simple classifiers, kernel density classification, a minimaxprobability machine (MPM), multi-class classification, multi-labelclassification, a Gaussian Process classification and regression,Bayesian statistical analysis, a Naive Bayes classifier, and neuralnetworks for regression and classification. While some of thesesupervised learning algorithms are linear, it should be understood thatone or more additional non-linear versions may be derived using the same“kernel-methodology”, as previously described for the SVM, leading to aspectrum of kernel-based learning methods, for example, kernel FDA,kernelized logistic regression, the kernelized perceptron algorithm,etc. One or more of these non-linear versions may be used to perform thestatistical analysis.

Unsupervised learning techniques may include a kernel density estimation(using, for example, Parzen windows or k-nearest neighbors), moregeneral density estimation techniques, quantile estimation, clustering,spectral clustering, k-means clustering, Gaussian mixture models, analgorithm using hierarchical clustering, dimensionality reduction, suchas principal component analysis or PCA, multi-dimensional scaling (MDS),isomap, local linear embedding (LLE), self-organizing maps (SOM),novelty detection (also referred to as single-class classification, suchas single-class SVM or single-class MPM), canonical correlation analysis(CCA), independent component analysis (ICA), factor analysis, and/ornon-parametric Bayesian techniques like Dirichlet processes. As notedabove for the supervised learning techniques, one or more additionalnon-linear versions of one or more linear unsupervised learningtechniques may be used to perform the statistical analysis, such askernel PCA, kernel CCA and/or kernel ICA.

In some embodiments, at least a portion of the statistical analysis,such as determination of one or more statistical relationships and/oridentification of one or more association variables may include spectralanalysis. For example, a Fourier transform or a discrete Fouriertransform may be performed on the temporal onsets, one or more patternsof occurrence of one or more variables, and/or one or more patterns ofoccurrence of one or more compound variables. Analysis in the frequencydomain may allow patterns in at least some of the data, such an impactof a woman's menstrual cycle, to be determined.

In some embodiments, determination of one or more statisticalrelationships and/or identification of one or more association variablesmay include the use of design of experiments. For example, at least thefirst individual may be exposed to a set of variables and/or compoundvariables in accordance with a temporal sequence that corresponds to anorthogonal array.

In some embodiments, at least a portion of the statistical analysisand/or identification of one or more association variables may beimplemented using one or more filters, including analog filters, digitalfilters, adaptive filters (using, for example, a least square error orgradient approach, such as steepest decent), and/or neural networks. Theone or more filters may be implemented using one or more DSPs. In someembodiments, the statistical analysis and/or identification of one ormore association variables may be implemented in hardware, for example,using one or more ASICs, and/or software.

FIG. 14 is a block diagram illustrating an embodiment of a signalprocessing circuit 1400 for determining one or more statisticalrelationships and/or identifying one or more association variables.Presence (coded with 1s) and absence information (coded with −1s) forone or more variables 1410 are selectively coupled using selectioncircuit 1416 to one or more filters H_(i) 1418. The selection circuit1416 may be a multiplexer. The filters H_(i) 1418 may perform spectralmodification, such as limiting one or more of the variables 1410 to oneor more time intervals, or one or more sequences of time intervals. Thefilters H_(i) 1418 may convert the presence and absence information forone or more of the variables 1410 into one or more patterns ofoccurrence.

The filters H_(i) 1418 may be adaptive. The adaptation may be inaccordance with temporal onsets 1412 and/or an error 1426. Theadaptation may include one or more time intervals, such as the firsttime intervals 912 (FIG. 9A), and/or one or more offsets, such as theoffset 924 (FIG. 9A). In some embodiments, the adaptation may minimizeor reduce the error 1426 or a portion of the error 1426. In theembodiments for migraine, for example, the adaptation may reduce apredicted number of migraines, a predicted severity, a predictedduration, and/or a predicted frequency.

Outputs from one or more of the filters H_(i) 1418 may be coupled tofilter H_(B) 1420. The filter H_(B) 1420 may perform additional spectralmodification. As a consequence, an arbitrary filtering operation may beimplemented using one or more of the filters H_(i) 1418 and/or thefilter H_(B) 1420. The filter H_(B) 1420 may determine a pattern ofoccurrence for one or more variables 1410 and/or one or more compoundvariables.

The temporal onsets 1412 may be filtered using filter H₃ 1418-3.Comparisons between an output of filter H₃ 1418-3 and an output of thefilter H_(B) 1420 may be performed using statistical analysis element1424. In some embodiments, the statistical analysis element 1424 may bea comparator. Statistical analysis element may implement one or morestatistical analysis techniques, such as the log likelihood ratio. Thestatistical analysis element 1424 may generate the error 1426. The error1426 may be a scalar, a vector, or a matrix. In some embodiments, thestatistical analysis element 1424 may perform a relative time shiftingof the output of filter H₃ 1418-3 and the output of the filter H_(B)1420. In an exemplary embodiment, the statistical analysis element 1424may determine one or more statistical relationships between the temporalonsets 1412 and one or more patterns of occurrence of one or morevariables and/or one or more compound variables. The one or morestatistical relationships may be determined sequentially and/orsubstantially concurrently. The error 1426 may correspond to the one ormore statistical relationships.

In some embodiments, one or more optional additional inputs, such asoptional additional input 1414, may be filtered using one or morefilters, such as filter H₄ 1418-4, and/or combined with the temporalonsets 1412 using a filter, such as filter/combiner H₅ 1422. An outputfrom the filter/combiner H₅ 1422 may be included in the analysisperformed by the statistical analysis element 1424. The one or moreoptional additional inputs may allow inclusion of cross-terms. In someembodiments, the one or more optional additional inputs may includeother disease symptoms and/or disease conditions.

While a single output is shown for the filter H_(B) 1420, there may beadditional outputs that are used by the statistical analysis element1424. Similarly, there may be additional outputs from thefilter/combiner H₅ 1422 that are used by the statistical analysiselement 1424. While embodiment 1400 uses presence and absenceinformation in the one or more variables 1410, the temporal onsets 1412,and the optional additional input 1414, in some embodiments one or moreof these items may only use presence information. Embodiment 1400 mayinclude fewer elements or additional elements, a position of at least anelement may be changed, and/or functions of two or more elements may becombined into a single element.

Attention is now directed to embodiments of processes for implementingthe collection of information during the data-collection time interval,the determining of one or more statistical relationships, theidentification of one or more association variables, and/or theproviding of recommendations to at least the first individual and/or atleast the second individual. FIG. 15 is a flow diagram illustrating anembodiment 1500 of a process for collecting information. A deviceincluding a set of predetermined questions may be optionally provided(1510). Configuration instructions may be optionally received (1512). Asubset of pre-determined questions may be asked, one or more timesduring a time interval, in accordance with the configurationinstructions (1514). Answers may be optionally pre-selected inaccordance with an answer history and/or default answers (1516). Answersto the subset of pre-determined questions may be received one or moretimes during the time interval (1518). Answers to the subset ofpre-determined questions may be transmitted one or more times during thetime interval (1520). Operations in embodiment 1500 may be optionallyrepeated, one or more times (1522). The process in embodiment 1500 mayinclude fewer operations or additional operations. A position of atleast one operation may be changed. Two or more operations may becombined into a single operation.

FIG. 16 is a flow diagram illustrating an embodiment 1600 of a processfor determining one or more association variables. Presence or absenceof one or more variables may be optionally determined in accordance withone or more thresholds (1610). A subset of temporal onsets may beoptionally identified (1612). Pattern(s) of occurrence of one or morevariables and/or one or more compound variables may be determined(1614). Statistical relationship(s) between temporal onsets or thesubset of temporal onsets and the pattern(s) of occurrence may bedetermined (1616). The variable(s) and/or the compound variable(s) maybe optionally ranked in accordance with the statistical relationship(s)(1618). The variables may be optionally ranked in accordance with anumber of occurrences of the variables in statistically significantstatistical relationships (1620). One or more association variablesand/or sets of association variables may be identified (1622). One ormore additional association variables may be optionally determined oridentified in accordance with the one or more association variables(1624). Operations in embodiment 1600 may be optionally repeated one ormore times (1626). The process in embodiment 1600 may include feweroperations or additional operations, a position of at least oneoperation may be changed, and/or two or more operations may be combinedinto a single operation.

FIG. 17 is a flow diagram illustrating an embodiment 1700 of a processfor providing recommendation(s) and/or report(s). Temporal onsets andpattern(s) of occurrence of one or more variables may be transmitted(1714) from a client computer 1710 to a server computer 1712. Thetemporal onsets and the pattern(s) of occurrence of one or morevariables may be received (1716). One or more statistical relationshipsmay be determined (1718). One or more recommendation(s) and/or report(s)may be transmitted (1720) from the server 1712 to the client computer1710. The one or more recommendation(s) and/or report(s) may be received(1722). The one or more recommendations and/or report(s) may bepresented (1724).

In some embodiments, the one or more recommendations and/or report(s)may include information corresponding to a first variable during a firstset of time intervals and the second variable during a second set oftime intervals. A respective time interval in a respective set of timeintervals may precede each of the temporal onsets in the one or moretemporal onsets. The first variable and the second variable may beassociated with a medical condition, i.e., they may be associationvariables. For example, the first variable and the second variable maybe migraine triggers, and at least the first individual may be advisedto avoid exposure to at least one of these variables during a given timeinterval (such as one, two or three days). A data structure thatincludes respective variables associated with a medical condition isdiscussed further below with reference to FIG. 25.

The process in embodiment 1700 may include fewer operations oradditional operations, a position of at least one operation may bechanged, and/or two or more operations may be combined into a singleoperation.

FIG. 18 is a flow diagram illustrating an embodiment 1800 of a processfor providing one or more reports. A request for a report may beoptionally transmitted (1814) from a client computer 1810 to a servercomputer 1812. The request for the report may be optionally received(1816). One or more reports may be generated (1818). The one or morereports may be transmitted (1820) from the server 1812 to the clientcomputer 1810. The one or more reports may be received (1822). The oneor more reports may be presented (1824). The process in embodiment 1800may include fewer operations or additional operations, a position of atleast one operation may be changed, and/or two or more operations may becombined into a single operation.

FIG. 19 is a flow diagram illustrating an embodiment 1900 of a process.An individual, such as at least the first individual, may be optionallyprovided with one or more potential migraine triggers that theindividual is to consume (1910). One or more migraine triggers may bedetermined for the individual (1912). This determining may include theanalysis techniques described previously and/or analysis of anoccurrence (including a presence and/or an absence) of one or moremarkers (such as deoxyribonucleic acid and/or ribonucleic acid) in oneor more biological samples taken from the individual (as describedfurther below). For example, the one or more markers may correspond to amigraine trigger associated with one or more receptors for estrogen orone or more receptors for a protein associated with egg white.

As previously discussed with reference to FIG. 13, the one or moremigraine triggers may be optionally associated with a set ofpre-determined migraine triggers that are associated with a group ofindividuals (1914). A set of questions may be optionally determined inaccordance with the pre-determined set of migraine triggers (1916). Theconfiguration instructions may correspond to the set of questions.Furthermore, one or more medicines may be optionally recommended to theindividual in accordance with the one or more migraine triggers and/orthe set of pre-determined migraine triggers (1918). For example, anantihistamine (such as periactin or its generic equivalent) may beprescribed for at least the first individual if an allergy or a foodsensitivity is determined to be a migraine trigger for at least thefirst individual. The process in embodiment 1900 may include feweroperations or additional operations. Furthermore, a position of at leastone operation may be changed, and/or two or more operations may becombined into a single operation.

As discussed previously, in some embodiments one or more foods consumed(such as mayonnaise) may be mapped to basic constituents (egg, vinegar,and/or mustard) and/or elemental constituents (minerals, fats,carbohydrates, and proteins). Alternatively, the mapping may be used inreverse (i.e., from one or more constituents to compound foods thatcontain these constituents) to determine the pattern of occurrence for avariable, such as mayonnaise, that occurs in many foods or dishes. Thesemappings may use a data structure such as that discussed below withreference to FIG. 24.

One or more mapping operations may utilize the technique shown in FIG.20, which illustrates an embodiment 2000 of a process for determiningitems that include a variable. Instances of the variable may bedetermined in a data structure (2010). One or more items that includethe variable may be identified (2012). The one or more items may bedefined as a subsequent version of the variable (2014). Operations inembodiment 2000 may be optionally repeated one or more times (2016), forexample, until a number of iterations are performed, a probabilityassociated with items identified in operation 2012 in a given iterationis less than a pre-determined value, or no instances of items areidentified in operation 2012 in the given iteration.

The process in embodiment 2000 may include fewer operations oradditional operations, a position of at least one operation may bechanged, and/or two or more operations may be combined into a singleoperation.

The embodiment 2000 of the process may be better understood in thecontext of FIG. 24, which is a block diagram illustrating an embodimentof a data structure 2400. The data structure 2400 includes a pluralityof compound items 2410. A respective compound item, such as compounditem 2410-1, may include a plurality of constituent items or ingredients2412. For example, mayonnaise may include egg, vinegar, and/or mustard.Some compound items, such as compound item 2410-M, may also include oneor more compound items, such as the compound item 2410-1, asingredients. In some embodiments, one or more ingredients 2412 haveassociated probabilities (not shown) that indicate an uncertainty in thepresence of the one or more ingredients 2412 in the compound items 2410.For example, a certain ingredient may occur in a fraction of the recipesfor a dish. The data structure 2400 may include fewer or additionalelements, a position of one or more elements may be changes, and/or twoor more elements may be combined into a single element.

Referring back to FIG. 20, instances of ingredient 2412-1 may bedetermined (2010). The compound item 2410-1 that includes the ingredient2412-1 may be identified (2012). The compound item 2410-1 may be definedas a subsequent version of the variable (2014). The operations may berepeated (2016), which will identify the compound item 2410-M asincluding the compound item 2410-1. The operations may be repeated untilone or more of the tree branches in the hierarchy terminate (forexample, the operation 2012 does not identify any additional compounditems). In some embodiments, a respective branch may terminate if aprobability associated with an instance of the variable is less than apre-determined value. As described previously, one or more ingredients2412 may have associated probabilities that are less than 1. As theoperations are repeated (2016), a product of the probabilities for theingredients 2412 and/or compound items 2410 in a respective branch maybe determined (for example, by multiplying the probabilities of theidentified ingredients 2412 and/or compound items 2410 in the respectivebranch), and if the total probability is less than the pre-determinedvalue (such as 50%), the respective branch may be terminated.

The set of identified compound items (including the compound items2410-1 and 2410-M) are the foods, dishes or beverages that include theingredient 2412-1. As discussed previously, this set may be used todetermine the pattern of occurrence for the ingredient 2412-1 and/or fora compound variable. In some embodiments, duplicate entries may beexcluded from the set of identified compound items. In some embodiments,duplicates may be excluded after each iteration (2016) of the operationsin the process.

For some medical conditions, food products (including foods, dishesand/or beverages) may be developed in accordance with the associationvariables that are determined for one or more groups of individuals. Therecommendations provided to at least the first individual may,therefore, include one or more food products in a category of foodproducts that at least the first individual may purchase and/or consumeto reduce and/or eliminate one or more symptoms associated with such amedical condition. In some diseases, such as migraines, there may be twoor more categories of food products. Food products in a respectivecategory may correspond to one or more groups of individuals that haverespective migraine triggers (such as group I 1310 in FIG. 13). Asdiscussed previously with reference to FIG. 13, there may be overlap inthe migraine triggers for at least two groups. In some embodiments,therefore, food products in at least two of the categories may bedeveloped in accordance with at least some of the migraine triggers thatare common. In some embodiments, however, food products in at least someof the categories may be developed in accordance with unique migrainetriggers, i.e., there may not be overlap in the migraine triggers for atleast some of the categories.

In some embodiments, the respective food product may exclude at leastsome of the respective migraine triggers and/or one or more itemsrelated to at least one or more of the respective migraine triggers(such as a food in the same food groups as one of the respectivemigraine triggers). In some embodiments, the respective food product mayinclude amounts or quantities of at least some of the respectivemigraine triggers and/or amounts or quantities of one or more itemsrelated to at least one or more of the respective migraine triggers thatare less than corresponding pre-determined values (for example, 1, 2, 5,10, 25 and/or 50%, by weight, of the respective food product). Therespective migraine triggers and/or one or more items related to atleast one or more of the respective migraine triggers may include foods,chemicals, and/or ingredients in foods. The respective migraine triggersand/or one or more items related to at least one or more of therespective migraine triggers may include an amino acid, an enzyme and/ora protein. Food products in the two or more categories may includesubstitutes for the respective migraine triggers and/or one or moreitems related to at least one or more of the respective migrainetriggers. Some of these alternatives are illustrated in Table V. For arespective migraine trigger, none, one, or more than one substituteingredient may be used.

TABLE V Migraine triggers and substitute ingredients for use in foodproducts. Migraine Triggers Substitute Ingredients Apple Pear, Waterchestnuts Bacon Textured soy protein, Mycoprotein Basil Fennel andCoriander seed Beef; Chicken Textured soy protein Blueberry RaspberryBroccoli Cauliflower Butter Margarine Carrot Sweet potato CeleryCeleriac (celery root) Champagne; Red wine Ver jus (unfermented grapejuice) Cheese Cheese food, Soy cheese Chocolate Carob Citric acid Malicacid (apple acid) Clementine Mandarin Coffee Chicory Crab Surimi CreamNon-dairy creamer Cream cheese; Goat Tofu (soft) cheese; Yogurt CucumberHoneydew Green onion Chive Jelly Flavored gelatin, Pectin gel LettuceCabbage, Kale, Spinach, Beet Greens Milk Soy milk (non-dairy) Monosodiumglutamate Ribotide ™, salt Mustard Powdered reconstituted mustardOregano Epazote Peas Legumes (kidney, cannellini, garbanzo, etc.) Ricemilk Soy milk (non-dairy) Salad dressing Soy-based non-acidified sauceStrawberry Watermelon Sweet potato Yucca, Plantain

In some embodiments, the respective food product may include an amountof one or more additional items or compounds that is greater than apre-determined value. The one or more additional items or compounds mayreduce an efficacy of the one or more migraine triggers to induce amigraine in at least the first individual. The one or more additionalitems or compounds may include an amino acid, an enzyme and/or aprotein. In an exemplary embodiment, the respective migraine triggersinclude tannins. The one or more additional items or compounds may,therefore, include praline. Praline may chemically interact with tanninsto produce compounds that are less effective as migraine triggers and/orthat are not migraine triggers. The amount or quantity or praline may beselected such that there is sufficient praline to neutralize tannins inthe respective food product.

FIG. 21 is a flow diagram illustrating an embodiment 2100 of a processfor providing food products. A first set or category of food productsthat contain less than a first amount of at least some migraine triggersin a first set of migraine triggers are provided (2110). The first setor category of food products may be intended for consumption by membersof a first group of individuals that respond to the first set ofmigraine triggers. For example, one or more migraine triggers in thefirst set of migraine triggers may, at least in part, induce a migrainein one or more members of the first group of individuals. A second setor category of food products that contain less than a second amount ofat least some migraine triggers in a second set of migraine triggers areprovided (2112). The second set or category of food products may beintended for consumption by members of a second group of individualsthat respond to the second set of migraine triggers. The process inembodiment 2100 may include fewer operations or additional operations, aposition of at least one operation may be changed, and/or two or moreoperations may be combined into a single operation.

Attention is now directed towards alternative applications for theembodiments of the process and apparatus for collecting information,determining one or more statistical relationships, identifying one ormore association variables, and providing one or more recommendationsand/or one or more reports. In some embodiments, one or more fees may becharged for offering the service of collecting the information and/oridentifying one or more association variables (such as one or moremigraine triggers) for at least the first individual. In someembodiments, the one or more fees may be in accordance with a costsavings associated with a reduced usage of one or more pharmacologicalagents (such as one or more acute and/or preventive therapies). The oneor more fees may be collected from at least the first individual, atleast the second individual, and/or one or more insurance providers. Insome embodiments, information associated with the one or more identifiedassociation variables may be sold to third parties. In some embodiments,advertising may be presented to at least the first individual and/or atleast the second individual during the collection of information, theproviding of one or more recommendations and/or the providing of one ormore reports. Fees may be charged to advertisers for such services.

In some embodiments, at least the first individual may be associatedwith one or more groups, such as one or more groups of migrainepatients, in accordance with one or more identified associationvariables (such as migraine triggers). A respective group may beanalyzed to determine one or more existing or new acute and/orpreventive therapies that may provide improved efficacy for therespective group. Using migraine as an exemplary embodiment, improvedefficacy may include a reduction in migraine frequency, a reduction inmigraine severity, a reduction in migraine duration, a reduction inrecurrence, a reduction in one or more adverse reactions or sideeffects, a reduction in the use of one or more pharmacological agents,and/or an improved efficacy in aborting one or more migraine attacksrelative to other acute and/or preventive therapies.

In some embodiments, association with one or more groups and/or analysisof the respective group may include statistical analysis and/ordetermining a presence or an absence of one or more biological markersin at least the first individual, including genetic material,deoxyribonucleic acid, ribonucleic acid (such as messenger ribonucleicacid), one or more genes, one or more proteins, and/or one or moreenzymes that may be common to the respective group and/or two or moregroups. The one or more biological markers may correspond to estrogen,one or more enzymes that assist in the digestion of food (for example,Lactase enzyme), one or more proteins, one or more peptides orpolypeptides that include up to 4 or 5 amino acids or more, histamine,one or more antigens, and/or a blood-brain barrier carrier-mediatedtransporter (such as those for glucose and/or amino acids). The one ormore biological markers may be determined by testing one or morebiological samples, including a blood sample, a urine sample, a stoolsample, a saliva sample, a sweat sample, a mucus sample, a skinscrapping, and/or a tear. The one or more biological samples may beanalyzed using chemical analysis, genetic analysis (such as geneticsequencing), nuclear quadrapole resonance, nuclear magnetic resonance,and/or electron spin resonance. In some embodiments, one or morepatients that have been diagnosed with a respective disease, such asmigraine, may be tested for the one or more biological markers toassociate the one or more patients with one or more of the groups ofpatients, and to recommend one or more pharmacological agents (such asone or more acute pharmacological agents, for example, a respectivefamily of triptans, and/or one or more preventive therapies) that mayoffer improved efficacy relative to other pharmacological agents for theone or more patients. Such a test or tests, based on the one or morebiological markers, may reduce or eliminate the current approach oftrial and error in searching for one or more pharmacological agents forpatients, such as one or more effective acute and/or preventivetherapies, which results in delays in patient treatment and additionalexpense. In some embodiments, such a test may be used in conjunctionwith or independently of the previously described embodiments ofdetermining association variables.

In some embodiments, the information collected during thedata-collection time interval may be analyzed to determine one or moresubgroups within a population of patients, such as the group of migrainepatients mentioned above. The one or more subgroups may be determinedbased on the one or more identified association variables (such asmigraine triggers), an efficacy of one or more pharmacological agents(such as one or more acute and/or preventive therapies), side effects oradverse reactions to one or more pharmacological agents, and/or patientsymptoms (such as migraine severity, migraine duration, and/or migrainefrequency). The subgroups may be determined using statistical analysisand/or determining a presence or an absence of the one or morebiological markers. In some embodiments, the one or more subgroups maybe used to study drug interactions in a real-world setting and patientpopulation. In some embodiments, the one or more subgroups may beindicative of underlying polymorphism in a genetic basis for arespective disease. Information corresponding to the one or moresubgroups may be sold to a third party, for example, for use inmolecular biology studies of the respective disease, the development ofone or more pharmacological agents, and/or a management of costsassociated with the disease.

In an exemplary embodiment, genetic polymorphism for migraine may bedetermined. Migraine is a genetically heterogeneous (polygenetic)disorder. While there is a strong familial aggregation of migraine (itruns in families) and there is increased concordance for the disease inmono-zygote twins over di-zygote twins, suggesting that it has asignificant genetic component, in part it may be explained byenvironmental determinants. Thus, heritability estimates are calculatedto be between 40 and 60%. The complex genetics of migraine(heterogeneity) may have hampered gene identification. Grouping migrainepatients into one or more subgroups (i.e., classifying the patients)based on identified migraine triggers may aid in the identification ofone or more genetic bases of and/or in the determination of geneticinformation for this disease.

While embodiments of apparatuses and related methods for determining oneor more association variables have been described, the apparatuses andrelated methods may be applied generally to determine statisticalrelationships between one or more temporal onsets corresponding to oneor more events and patterns of occurrence of one or more variablesand/or one or more compound variables in a wide variety of statisticallearning problems, in medicine, psychology, statistics, engineering,applied mathematics and operations research. In other embodiments, theapparatuses and related methods may be applied generally to determinestatistical relationships between an independent variable (a result) andone or more dependent variables that are time independent or stationaryover at least a time interval, such as the data collection timeinterval.

Attention is now directed to embodiments of data structures that may beused in implementing the collection of information during thedata-collection time interval, the determining of one or morestatistical relationships, the identification of one or more associationvariables, and/or the providing of one or more recommendations and/orone or more reports to at least the first individual and/or at least thesecond individual. FIG. 22 is a block diagram illustrating an embodimentof a questionnaire data structure 2200. The questionnaire data structure2200 may include one or more modules 2210. A respective module, such asmodule 2210-1, may include entries for one or more questions 2212, oneor more classifications 2214 for the questions 2212 (such as primary orsecondary, or general or specific), one or more default answers 2216,and/or one or more answer histories 2218. The questionnaire datastructure 2200 may include fewer or addition modules and/or entries. Aposition of at least a module and/or a position of at least an entry maybe changed. Furthermore, two or more modules may be combined into asingle module, and/or two or more entries may be combined into a singleentry.

FIG. 23 is a block diagram illustrating an embodiment of a datastructure 2300. At least a portion of the data structure 2300 may beincluded in the server computer 300 (FIG. 3), the computer 400 (FIG. 4),and/or the device 500 (FIG. 5). The data structure 2300 may include oneor more sets of categories. A respective set of categories maycorrespond to at least the first individual. The respective set ofcategories may include identification 2310 for at least the firstindividual, meta data 2312 (such as relevant demographic, billing and/ormedical history data for at least the first individual), configurationinstructions 2314, temporal onsets 2316, variable(s) 2322, derivedvariable(s) 2324, compound variable(s) 2326, statistical relationships2328, optional rankings 2330, association variable(s) 2332, group(s) ofassociation variables 2334 and/or recommendations/reports 2336. Thetemporal onsets 2316, the variable(s) 2322, the derived variable(s)2324, and/or the compound variable(s) 2326 may include one or moreentries including time intervals 2318 and corresponding presence and/orabsence information 2320. The data structure 2300 may include fewer oraddition categories and/or entries, and two or more categories may becombined into a single category. Furthermore, a position of at least acategory and/or a position of at least an entry may be changed, and/ortwo or more entries may be combined into a single entry.

FIG. 25 is a block diagram illustrating an embodiment of a datastructure 2500. The data structure 2500 may be used in conjunction withthe analysis described above with reference to FIGS. 9 and 10, theassociation with groups described above with reference to FIG. 13,and/or the providing of recommendations and/or reports described abovewith reference to FIGS. 17 and 18. The data structure may include aplurality of compound variables 2510. A respective compound variable,such as compound variable 2510-1, may include two or more variables,such as variables 2512 and 2516, and corresponding time intervals 2514.The compound variables 2510 may include association variables for one ormore medical conditions. While the data structure 2500 includes twovariables for each compound vector, in other embodiments respectivecompound variables may include one, two or more variables.

In an exemplary embodiment, the compound variables 2510 includeassociation variables for migraines, such as migraine triggers, for oneor more individuals including at least the first individual. In anillustrative embodiment, the variable 2512 is tomato, time interval2514-1 is 48-72 hours prior to a migraine onset, the variable 2514 issalad dressing, and the time interval 2514-2 is 0-72 hours prior to themigraine onset. Thus, a migraine may, at least in part, be induced if atleast the first individual consumes a quantity of tomato (such as anormal or usual amount consumed by at least the first individual) 48-72hours earlier and if at least the first individual consumes a quantityof salad dressing (such as a normal or usual amount consumed by at leastthe first individual) 0-72 hours earlier. In another illustrativeembodiment, the variable 2512 is a barometric pressure change of ±8 mmHg in a 6 hour time interval, time interval 2514-1 is 48-72 hours priorto a migraine onset, the variable 2514 is an orange, and the timeinterval 2514-2 is 24-72 hours prior to the migraine onset. Thus, amigraine may, at least in part, be induced if at least the firstindividual is exposed to a barometric pressure change of ±8 mm Hg in a 6hour time interval 48-72 hours earlier and if at least the firstindividual consumes a quantity of an orange (such as a normal or usualamount consumed by at least the first individual) 24-72 hours earlier.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that the specificdetails are not required in order to practice the invention. Theforegoing descriptions of specific embodiments of the present inventionare presented for purposes of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed. Obviously many modifications and variations are possible inview of the above teachings. The embodiments were chosen and describedin order to best explain the principles of the invention and itspractical applications, the thereby enable others skilled in the art tobest utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the scope of the invention be defined by the followingclaims and their equivalents.

1. An apparatus to determine one or more migraine triggers associatedwith migraines, comprising: at least one processor; at least one memoryconfigured to store information associated with a set of temporal onsetsthat is associated with headaches experienced by at least oneindividual, and information associated with a pattern of occurrence of avariable associated with at least the one individual; and at least oneprogram module, the program module stored in the memory and configurableto be executed by the processor, the program module including:instructions for selecting a subset of temporal onsets in the set oftemporal onsets based on one or more characteristics of different typesof headaches, wherein the different types of headaches include migraineand recurrence migraine, wherein the subset of temporal onsets includesone or more onsets corresponding to one or more migraines in theheadaches experienced by at least the one individual, wherein the set oftemporal onsets includes the subset of temporal onsets and one or moretemporal onsets corresponding to one or more additional headaches in theheadaches experienced by at least the one individual, wherein the one ormore additional headaches include one or more recurrence migraines,wherein the one or more characteristics of recurrence migraine includethat a given recurrence migraine corresponds to a given temporal onsetin a given group of two or more temporal onsets in the set of temporalonsets that is within a predetermined time interval after an initialtemporal onset in the given group of two or more temporal onsets, andwherein the group of two or more temporal onsets can be associated witha single migraine attack; instructions for determining a statisticalrelationship between the subset of temporal onsets and the pattern ofoccurrence of the variable in an underdetermined problem in which thereare more variables than temporal onsets in the set of temporal onsets,wherein a severity of the underdetermined problem is increased byexcluding the one or more temporal onsets corresponding to one or moreadditional headaches; and instructions for identifying the variable as amigraine trigger in accordance with the statistical relationship.
 2. Theapparatus of claim 1, wherein the statistical relationship includescontributions from presence and absence information in the pattern ofoccurrence of the variable.
 3. The apparatus of claim 1, wherein the oneor more additional headaches include one or more tension headaches. 4.The apparatus of claim 1, further comprising instructions for excludingat least one of the temporal onsets in the set of temporal onsets fromthe subset of temporal onsets due to missing data in the pattern ofoccurrence of the variable that was not reported by at least the oneindividual.
 5. The apparatus of claim 1, wherein the pattern ofoccurrence of the variable is during a set of time intervals, andwherein a respective time interval in the set of time intervals precedesa corresponding respective temporal onset in the subset of temporalonsets.
 6. The apparatus of claim 5, wherein time intervals in the setof time intervals are offset in time from temporal onsets in the subsetof temporal onsets.
 7. The apparatus of claim 1, the program modulefurther including instructions for receiving information including theset of temporal onsets and the pattern of occurrence of the variable. 8.The apparatus of claim 1, the program module further includinginstructions for providing recommendations to one or more individuals inaccordance with the migraine variable.
 9. The apparatus of claim 1,wherein the determining uses a non-parametric statistical analysistechnique selected from a group consisting of a chi-square analysistechnique, a log-likelihood ratio analysis technique and a Fisher'sexact probability analysis technique.
 10. The apparatus of claim 1,wherein the determining uses a supervised learning technique selectedfrom a group consisting of a support vector machines (SVM) analysistechnique, a Lasso analysis technique and a classification andregression tree (CART) analysis technique.
 11. The apparatus of claim 1,wherein the predetermined time interval is less than or equal to 24hours.
 12. The apparatus of claim 1, the program module furtherincluding instructions for determining statistical relationships for aplurality of variables in the underdetermined problem.
 13. The apparatusof claim 12, the program module further including instructions fordetermining a first ranking of the plurality of variables based on atleast a subset of the statistical relationships, wherein the firstranking is based on the number of occurrences of the variables in the atleast the subset of the statistical relationships.
 14. The apparatus ofclaim 13, the program module further including instructions forsubtracting a second ranking from the first ranking, wherein the secondranking corresponds to a background signal.
 15. The apparatus of claim1, wherein a respective entry in a pattern of occurrence of the variableis considered present if the respective entry approximately exceeds athreshold.
 16. The apparatus of claim 1, wherein the migraine trigger atleast in part induces a migraine in at least the one individual if atleast the one individual is exposed to the migraine trigger.
 17. Theapparatus of claim 16, the program module further including instructionsfor identifying one or more additional migraine triggers of at least theone individual based on the presence of the identified migraine triggerin one or more groups of migraine triggers that were previouslydetermined for one or more other individuals.
 18. The apparatus of claim1, wherein entries in the pattern of occurrence of the variable duringtime intervals associated with ongoing durations of each of themigraines corresponding to the subset of temporal onsets are excludedwhen the statistical relationship is determined.
 19. A non-transitorycomputer-program product for use in conjunction with a computer system,the computer-program product comprising a computer-readable storagemedium and a computer-program mechanism embedded therein for determiningone or more migraine triggers associated with migraines, thecomputer-program mechanism including: instructions for selecting asubset of temporal onsets in a set of temporal onsets that is associatedwith headaches experienced by at least one individual based on one ormore characteristics of different types of headaches, wherein thedifferent types of headaches include migraine and recurrence migraine,wherein the subset of temporal onsets includes one or more onsetscorresponding to one or more migraines in the headaches experienced byat least the one individual, wherein the set of temporal onsets includesthe subset of temporal onsets and one or more temporal onsetscorresponding to one or more additional headaches in the headachesexperienced by at least the one individual, wherein the one or moreadditional headaches include one or more recurrence migraines, whereinthe one or more characteristics of recurrence migraine include that agiven recurrence migraine corresponds to a given temporal onset in agiven group of two or more temporal onsets in the set of temporal onsetsthat is within a predetermined time interval after an initial temporalonset in the given group of two or more temporal onsets, and wherein thegroup of two or more temporal onsets can be associated with a singlemigraine attack; instructions for determining a statistical relationshipbetween the subset of temporal onsets and a pattern of occurrence of avariable associated with at least the one individual in anunderdetermined problem in which there are more variables than temporalonsets in the set of temporal onsets, wherein a severity of theunderdetermined problem is increased by excluding the one or moretemporal onsets corresponding to one or more additional headaches; andinstructions for identifying the variable as a migraine trigger inaccordance with the statistical relationship.
 20. An apparatus todetermine one or more migraine triggers associated with migraines,comprising: means for computing; means for storing informationassociated with a set of temporal onsets that is associated withheadaches experienced by at least one individual, and informationassociated with a pattern of occurrence of a variable associated with atleast the one individual; and at least one program module mechanism, theprogram module mechanism stored in at least the means for storing andconfigurable to be executed by at least a computing means, at least theprogram module mechanism including: instructions for selecting a subsetof temporal onsets in the set of temporal onsets based on one or morecharacteristics of different types of headaches, wherein the differenttypes of headaches include migraine and recurrence migraine, wherein thesubset of temporal onsets includes one or more onsets corresponding toone or more migraines in the headaches experienced by at least the oneindividual, wherein the set of temporal onsets includes the subset oftemporal onsets and one or more temporal onsets corresponding to one ormore additional headaches in the headaches experienced by at least theone individual, wherein the one or more additional headaches include oneor more recurrence migraines, wherein the one or more characteristics ofrecurrence migraine include that a given recurrence migraine correspondsto a given temporal onset in a given group of two or more temporalonsets in the set of temporal onsets that is within a predetermined timeinterval after an initial temporal onset in the given group of two ormore temporal onsets, and wherein the group of two or more temporalonsets can be associated with a single migraine attack; instructions fordetermining a statistical relationship between the subset of temporalonsets and the pattern of occurrence of the variable in anunderdetermined problem in which there are more variables than temporalonsets in the set of temporal onsets, wherein a severity of theunderdetermined problem is increased by excluding the one or moretemporal onsets corresponding to one or more additional headaches; andinstructions for identifying the variable as a migraine trigger inaccordance with the statistical relationship.